In computational complexity and latency con- strained emerging 5 G applications , e.g. , autonomous vehicles , haptic communications and enhanced reality , secret key generation ( SKG ) at the physical layer could be considered as an alternative to currently used key agreement schemes . In this framework , we study the optimal subcarrier scheduling in multicarrier systems when a subset of the subcarriers are used for SKG and the rest for data transmission , under both security and power constraints . Ethical thought experiments such as the trolley dilemma have been investigated extensively in the past , showing that humans act in utilitarian ways , trying to cause as little overall damage as possible . These trolley dilemmas have gained renewed attention over the past few years , especially due to the necessity of implementing moral decisions in autonomous driving vehicles ( ADVs ) . We conducted a set of experiments in which participants experienced modified trolley dilemmas as drivers in virtual reality environments . The Rapidly-exploring Random Tree ( RRT ) is a classical algorithm of motion planning based on incremental sampling , which is widely used to solve the planning problem of mobile robots . But it , due to the meandering path , the inaccurate terminal state and the slow exploration , is often inefficient in many applications such as autonomous road vehicles . To address these issues and considering the realistic context of autonomous road vehicles , this paper proposes a fast RRT algorithm that introduces an off-line template set based on the traffic scenes and an aggressive extension strategy of search tree . Modern cyber-physical systems are complex networked computing systems that electronically control physical systems . Autonomous road vehicles are an important and increasingly ubiquitous instance . Unfortunately , their increasing complexity often leads to security vulnerabilities . Recent developments toward selfdriving cars combined with technology for wireless inductive power transfer can enable electric vehicles that are fully autonomous with respect to operation and energy requirements . Furthermore , technology for wireless opportunity charging and dynamic on-road inductive power transfer can facilitate autonomous electric vehicles with unlimited driving range . This article discusses the technological progress toward self-driving vehicles and the potential capability for an autonomous energy supply enabled by dynamic on-road wireless power transfer . Both scientist and companies have paid attention to electric vehicles ( EVs ) as a clean energy to reduce the oil demands and gas emissions . On the other hand , autonomous electric vehicles have been seen as cutting-edge technology . Self-driving vehicles , and vehicle ride-sharing offered by autonomous EVs are transportation services that have surged quickly . In this paper , we presented a number of empirical evaluations of recent deep learning advances . Computer vision , combined with deep learning , has the potential to bring about a relatively inexpensive , robust solution to autonomous driving . To prepare deep learning for industry uptake and practical applications , neural networks will require large data sets that represent all possible driving environments and scenarios . document : Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes During the last half decade , convolutional neural networks ( CNNs ) have triumphed over semantic segmentation , which is a core task of various emerging industrial applications such as autonomous driving and medical imaging . However , to train CNNs requires a huge amount of data , which is difficult to collect and laborious to annotate . As crucial information , lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames . The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic , dice similarity coefficient and real-time performance . For lane departure detection , a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform . Digital libraries : Meeting place for high-level and low-level vision.- Computer vision in next generation image and video coding.- Video annotation : Computers watching video.- Video representations.- Analysis of left ventricular motion.- Applications of computer vision in space robotics.- Optical-flow estimation while preserving its discontinuities : A variational approach.- Space- and time-variant estimation approaches and the segmentation of the resulting optical flow fields.- Combining the hough transform and multiresolution MRF 's for the robust motion estimation.- Direct model-based image motion segmentation for dynamic scene analysis.- Modeling structured environments using robot vision.- Design trade-offs for real-time block-matching motion estimation algorithms.- Pixel matching and motion segmentation in image sequences.- On the motion problem of a rigid planar patch and its invariance.- Virtual space teleconferencing : Real-time detection and reproduction of 3D face and body images.- High-performance tracking with TRICLOPS.- Developing an active observer.- Active stereo vision system with foveated wide angle lenses.- A control theoretical approach to active vision.- Active rangefinding and recognition with Cubicscope.- Real-time vision is not frame-rate image analysis.- Active vision based stereo vision.- Motion of a stereo rig : Strong , weak and self calibration.- Color stereo vision : Use of appearance constraint and epipolar geometry for feature matching.- Unifying stereo , motion and object recognition via epipolar geometry.- Constructing a visual memory in terms of uniqueness.- Statistical models & algorithms for tomography.- Shape statistics and image analysis.- Cluster Monte Carlo algorithms and their applications.- Gibbs sampler and maximum likelihood estimation for unsupervised image segmentations.- Lane boundary detection using deformable templates : Effects of image subsampling on detected lane edges.- Clique finding relaxation labeling networks.- Rectifying structural matching errors.- Parallel distributed relaxation labeling.- Subgraph isomorphism detection in polynomial time on preprocessed model graphs.- Line postprocessing using probabilistic relaxation and relative measurements.- Multiple-view geometry and photometry.- Euclidean 3D reconstruction from stereo sequences with variable focal lengths.- Characterizing the stability of 3D invariants derived from 3D translational symmetry.- MORSE : An architecture for 3D object recognition based on invariants.- Finding human faces with a Gaussian mixture distribution-based face model.- Handwriting recognition.- An integrated model for evaluating the amount of data required for reliable recognition.- 3D object recognition using bidirectional modular networks.- A region extension method for natural outdoor scene interpretation.- Image processing techniques for crowd density estimation using a reference image.- Autonomous road vehicle guidance in normal traffic.- Evaluation of image sequences from outdoor scenes : Selected problems and solutions.- Vehicle localisation and discrimination in outdoor traffic scenes.- SWIM : A prototype environment for visual media retrieval.- Video segment indexing through classification and interactive view-based query.- View-based techniques for searching for objects and textures.- Graph grammar based object recognition for image retrieval.- Similarity matching.- A document image analysis and recognition system for Japanese family registration.- Applying handwriting recognition to US census forms . In this paper , a robust lane detection approach , that is primary and essential for driver assistance systems , is proposed to handle the situations where the lane boundaries in an image have relatively weak local contrast , or where there are strong distracting edges . The proposed lane detection approach makes use of a deformable template model to the expected lane boundaries in the image , a maximum a posteriori ( MAP ) formulation of the lane detection problem , and a particle swarm optimization algorithm to maximize the posterior density . The model parameters completely determine the position of the vehicle inside the lane , its heading direction , and the local structure of the lane . Because this method uses SVM , which has a better classified performance than BP(Back Propagation)neural network , it obtains a better detection result than that using BP neural network(H.Jeong , et al).Furthermore , this method uses PCNN to process the frames to remove the shadow in the road , reducing the effect of illumination variations . Experimental results show that this method can receive better lane detecting results than the trapezoidal model and BP neural network proposed by H.Jeong , et al. Furthermore , in preprocessing using PCNN removes the shadow in the road to reduce the effect of illumination variations . Experimental results show that our method can receive better lane detecting results than the trapezoidal model and BP proposed by H. Jeong , et al .. Due to the complexity of urban traffic scenes , false lane detections are highly caused by warning lines and signs whose shapes and colors are similar to the lane boundary . In this study , we improve the accuracy of the lane detection base on Hough , a score function based on the width between left and right lanes is proposed to obtain reliable lane detect results on urban traffic scene . Meanwhile , a list of candidate lanes is constructed at the least of execution time . In this paper , we propose a real-time multi-lane detection algorithm that is robust to the challenging lane condition . For basic lane detection part , we extract the lane features from images , and based on the features geometric uncertainty algorithm we can detect the lanes , guardrails , other vehicles in real-time . What ’ s more , the accuracy can be improved with the other vehicles direction information and guardrail depth information from stereo vision when the lane features are non-continuous or poor . The following paper deals with a lane centering system for motorways , which is implemented into a demonstrator vehicle in order to evaluate sensor performance and support the development process of assistance systems for lateral vehicle dynamics and lane detection sensors . The implemented system uses lane information acquired by a mono camera system , which is mounted behind the windshield of the demonstrator vehicle . In order to improve adaptability to different perspective conditions , a width-adaptive lane detection method is proposed . As a useful reference to reduce noises , vanishing point is widely applied in lane detection studies . However , vanishing point detection based on original image consumes many calculation resources . The effective detected region was selected with the combination of the regional contrast analysis and OTSU method , and then the image was segmented with adaptive threshold . The experimental result of all kinds of typical lane image detection shows that not only the real time request can be satisfied but also the adaptive capability and the robustness to the influence of discontinuous lane marking can be increased . To prepare deep learning for industry uptake and practical applications , neural networks will require large data sets that represent all possible driving environments and scenarios . We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection . We show how existing convolutional neural networks ( CNNs ) can be used to perform lane and vehicle detection while running at frame rates required for a real - time system . Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving . In this paper , we detail empirical evaluation on the data set we collect . In addition , we explain the neural network that we applied for detecting lanes and cars , as shown in Figure [ reference ] . section : Related Work This allows for the feature vector to cover the full stride size of pixels ; the end result is a grid mask detector of size where each element is pixels which covers the entire input image of size . subsection : Lane Detection The CNN used for vehicle detection can be easily extended for lane boundary detection by adding an additional class . The power assist suit is supposed to support the wearer so that it reduces the load of the radiation protection equipment during an operation . This paper proposes a power assist control method based on motion prediction using 9-axis motion sensors . The power assist suit enables rapid power assist because the motion sensor can detect the start of walking motion in real time . The model captured the behavioral decrease in tracking accuracy with both increasing trial duration ( exp 1 ; p < .001 ) and increasing disc speed ( exp 2 ; p < .001 ) , although predicted accuracy in exp 2 was slightly higher than what was found in the behavioral data ( p = .05 ) . We conclude that motion prediction , simulated here by simple Kalman filters , can describe tracking accuracy in two widely-studied MOT tasks . Meeting abstract presented at VSS 2015 . We use 3D object tracking to mine for more than 300k interesting vehicle trajectories to create a trajectory forecasting benchmark . Motion forecasting experiments ranging in complexity from classical methods ( k-NN ) to LSTMs demonstrate that using detailed vector maps with lane-level information substantially reduces prediction error . Our tracking and forecasting experiments represent only a superficial exploration of the potential of rich maps in robotic perception . In this work , we present LaserFlow , an efficient method for 3D object detection and motion forecasting from LiDAR . Unlike the previous work , our approach utilizes the native range view representation of the LiDAR , which enables our method to operate at the full range of the sensor in real-time without voxelization or compression of the data . This new kind of systems generates not only new possibilities but also new problems to be solved . Among them are real-time rendering , motion estimation and prediction , animation techniques like autonomous actors , new three-dimensional interaction techniques , and architectures that embody clear concepts and software engineering methodologies for such kind of systems . We analyze a number of existing systems that illustrate what we believe are core concepts and difficulties to be overcome . In Fig. 1 we show the tracking maneuver of a freshwater turtle as it strives to capture a moving fish . Turtles anticipate future position of a moving target by solving a motion prediction problem – a task that is believed to be initiated in the visual cortex . Visual inputs to the retina are routed through the geniculate before it hits the cortex ( see Fig. 2 ) . This paper addresses the motion prediction problem of a planetary vehicle under the following hypotheses : the vehicle must be able to move on both , natural and hostile terrains , it must have the ability of selecting and applying several navigation strategies depending on the characteristics of the terrain , and it must be capable of showing complex behaviors derived by both , its sophisticated mechanical structure and the vehicle/terrain physical interactions . An original method to simulate the dynamic behavior of the vehicle ( i.e. , vehicle/terrain interactions , sliding or gripping effects ... ) when executing motions having the previous characteristics is presented . Contemporary situational awareness problems such as automated normalcy learning for anomaly detection and motion behavior prediction are addressed with biologically-inspired processing , representation , and learning approaches . Issues and challenges are discussed and our responses to them described . It predicts two most important vehicle motion parameters ( forward and angular velocity ) . For this , we have adapted an existing architecture ( proposed by Zhang et al. ) and improved motion parameters prediction . A video dataset has been collected using android smartphone ( Pilotguru ) as capturing rash instances was necessary . This paper investigates a novel approach , target motion modeling through the grey prediction model , to compensate the uncertainty with the correction action of the robot . Firstly , with consideration of the nonlinear and non-gaussian characteristics of the environment and target tracking , the particle filter algorithm is introduced for motion prediction and estimation , and the target position in the robot vision can be calculated . Then , an intention inference based reinforcement learning control method is used to estimate the mapping from sensory information to appropriate robot action based on the characteristics of the method , which , do not need the model of the environment and the robot . Aiming at the local optimal problem caused by precocious particles in the path planning process of mobile robot by traditional particle swarm optimization algorithm ( PSO ) , a hybrid neural process-pso algorithm was constructed by integrating the idea of motion process prediction into PSO . The main idea is that in the next iteration of particle swarm individuals , the neural process is used to predict the individual location , increasing the diversity of particle swarm at the later stage of the iteration , and avoiding falling into local optimization too early , so as to improve the optimization ability of the algorithm . The proposed method is able to generate smooth human motions that resemble the original motion from the same viewpoint the sequence was taken , and look continuous from any other viewpoint . The core of the proposed system is the motion trend prediction for reconstruction . To focus the research effort on motion reconstruction , “ synthesized ” input is first employed to ensure that the reconstruction algorithm is developed and evaluated accurately . paragraph : Image alignment Image alignment is a key step in many computer vision tasks , including face verification , motion analysis , stereo matching , and object recognition . Alignment results in correspondence across different images by removing intraclass variability and canonicalizing pose . Spatio - temporal filters . Burt and Adelson lay out the theory of spatio - temporal models for motion estimation and Heeger provides a computational embodiment . While inspired by human perception , such methods did not perform well at the time . The chosen shape recognition algorithm is the Histogram of Oriented Gradients . This method has been proved to be capable to recognize complex objects and is widely used for pedestrian recognition . The complex objects we recognize are other humanoid robots ( in particular Naos ) , similar in complexity for their anthropomorphic shape but less variable than human beings . However , they can not detect crossing pedestrians when turning at intersections because the field of view is not wide enough . Thus , we propose to utilize a surround view camera system becoming popular by making it into stereo vision which is robust for the pedestrian recognition . However , conventional stereo camera technologies can not be applied due to fisheye cameras and uncalibrated camera poses . As a result of its important role in video surveillance , pedestrian attribute recognition has become an attractive facet of computer vision research . Because of the changes in viewpoints , illumination , resolution and occlusion , the task is very challenging . In surveillance scenarios , the pedestrian images are often facing poor resolution problems or the images are often suffered the occlusion problems . These problems make pedestrian attribute recognition more difficult . In order to solve this problem , we propose an improved pedestrian attribute recognition method based on hand-crafted feature . Pedestrian analysis plays a vital role in intelligent video surveillance and security-centric computer vision systems . Despite that the deep convolutional neural networks ( DCNNs ) achieved remarkable performance in computer vision , learning fine-grained features of pedestrian attribute tasks is a challenging task in complex surveillance scenarios . In this paper , we proposed a new DCNNs framework for pedestrian analysis , with the main idea of integrating different learning tasks of pedestrian body parts detection and pedestrian attribute classification , which called Hyper-pedestrian convolutional neural network ( HP-CNN ) . Additionally , the proposed HP-CNN bring some advantages : 1 ) Squeeze-and-excitation block ( SE-block ) strengthens the representational power of networks by selectively emphasising informative features ; 2 ) Multi-scale feature fusion concatenates more fine-grained information from both the low-level and high-level and enhances the contextual information from different convolutional layers . Experiments show that the proposed method significantly outperforms the SVM based method on the PETA database . Multi-label convolutional neural network for pedestrian attribute classification . The proposed MLCNN can simultaneously predict multiple pedestrian attributes . Our MTNet is an end-to-end deep learning framework , which all the parameters and losses can be jointly optimized . In our method we combine two tasks closely corresponding to person re-identification , pedestrian identity task and pedestrian attribute task , who provide complementary information from different perspective by integrating multi-context informations . Attribute focuses on some special aspects of a person , while identity pays more attention to overall contour and appearance . Pedestrian Attribute Recognition ( PAR ) is an important task in surveillance video analysis . In this paper , we propose a novel end-to-end hierarchical deep learning approach to PAR . Extracting visual attributes from a scene , such as the type of shoes , the type of clothing , carrying any object or not , or wearing any accessory etc . , is a challenging problem and an efficient solution holds the key to a great number of applications . In this paper , we present a multi-branch convolutional neural network that uses depthwise separable convolution ( DSC ) layers to solve the pedestrian attribute recognition problem . Researchers have proposed various solutions over the years making use of convolutional neural networks ( CNN ) , however , we introduce DSC layers to the CNN for the problem of pedestrian attribute recognition . This paper expands the strength of deep convolutional neural networks ( CNNs ) to the pedestrian attribute recognition problem by devising a novel attribute aware pooling algorithm . Existing vanilla CNNs can not be straightforwardly applied to handle multi-attribute data because of the larger label space as well as the attribute entanglement and correlations . Pedestrian detection and abnormal behavior detection is the computer for a given image and video , to determine whether there are pedestrians and their behavior is normal . Pedestrian detection is the basis and premise of pedestrian tracking , behavior analysis , gait analysis , pedestrian identity recognition and so on . A good pedestrian detection algorithm can provide strong support and guarantee for the latter . This joint learning framework is named Joint Learning of Parsing attention , Label attention , and Spatial attention for Pedestrian Attributes Analysis ( JLPLS-PAA , for short ) . Extensive comparative evaluations conducted on multiple large-scale benchmarks , including PA-100 K , RAP , PETA , Market-1501 , and Duke attribute datasets , further demonstrate the effectiveness of the proposed JLPLS-PAA framework for pedestrian attribute analysis . The capability for recognizing pedestrian semantic attributes , such as gender , clothes color and other semantic attributes is of practical significance in bank smart surveillance , intelligent transportation and so on . In order to recognize the key multi attributes of pedestrians in indoor and outdoor scenes , this paper proposes a deep network with dynamic weights and joint loss function for pedestrian key attribute recognition . First , a new multi-label and multi-attribute pedestrian dataset , which is named NEU-dataset , is built . document : Small - scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation A critical issue in pedestrian detection is to detect small - scale objects that will introduce feeble contrast and motion blur in images and videos , which in our opinion should partially resort to deep - rooted annotation bias . Motivated by this , we propose a novel method integrated with somatic topological line localization ( TLL ) and temporal feature aggregation for detecting multi - scale pedestrians , which works particularly well with small - scale pedestrians that are relatively far from the camera . section : Introduction Pedestrian detection is a critical problem in computer vision with significant impact on a number of applications , such as urban autonomous driving , surveillance and robotics . In recent years many works have been devoted to this detection task , however , there still leaves a critical bottleneck caused by various scales of pedestrians in an image . ‘ R - CNN with AlexNet ’ ’ with the distinction made clear by the context . During R - CNN training AlexNet is fine - tuned for the ( pedestrian ) detection task , and in a second step , the softmax output is replaced by a linear SVM . Unless otherwise specified , we use the default parameters of the open source , Caffe based , R - CNN implementation . However , these fine - grained attributes are very useful to distinguish the pedestrian pairs with small inter - class variations . Thus these DCNN models are not the best choice for pedestrian feature learning . Second , due to the pose variations and imperfect pedestrian detectors , the pedestrian image samples may be misaligned . We further evaluate the approach extensively for an image classification task , and present results on traditional deep learning image data sets - such as MNIST [ reference ] and SVHN [ reference ])- as well as on Office benchmarks [ reference ] , where domain - adversarial learning allows obtaining a deep architecture that considerably improves over previous state - of - the - art accuracy . Finally , we evaluate domain - adversarial descriptor learning in the context of person re - identification application [ reference ] , where the task is to obtain good pedestrian image descriptors that are suitable for retrieval and verification . We apply domainadversarial learning , as we consider a descriptor predictor trained with a Siamese - like loss instead of the label predictor trained with a classification loss .
In computational complexity and latency con- strained emerging 5 G applications , e.g. , autonomous vehicles , haptic communications and enhanced reality , secret key generation ( SKG ) at the physical layer could be considered as an alternative to currently used key agreement schemes . In this framework , we study the optimal subcarrier scheduling in multicarrier systems when a subset of the subcarriers are used for SKG and the rest for data transmission , under both security and power constraints .
Ethical thought experiments such as the trolley dilemma have been investigated extensively in the past , showing that humans act in utilitarian ways , trying to cause as little overall damage as possible . These trolley dilemmas have gained renewed attention over the past few years , especially due to the necessity of implementing moral decisions in autonomous driving vehicles ( ADVs ) . We conducted a set of experiments in which participants experienced modified trolley dilemmas as drivers in virtual reality environments .
The Rapidly-exploring Random Tree ( RRT ) is a classical algorithm of motion planning based on incremental sampling , which is widely used to solve the planning problem of mobile robots . But it , due to the meandering path , the inaccurate terminal state and the slow exploration , is often inefficient in many applications such as autonomous road vehicles . To address these issues and considering the realistic context of autonomous road vehicles , this paper proposes a fast RRT algorithm that introduces an off-line template set based on the traffic scenes and an aggressive extension strategy of search tree .
Modern cyber-physical systems are complex networked computing systems that electronically control physical systems . Autonomous road vehicles are an important and increasingly ubiquitous instance . Unfortunately , their increasing complexity often leads to security vulnerabilities .
Recent developments toward selfdriving cars combined with technology for wireless inductive power transfer can enable electric vehicles that are fully autonomous with respect to operation and energy requirements . Furthermore , technology for wireless opportunity charging and dynamic on-road inductive power transfer can facilitate autonomous electric vehicles with unlimited driving range . This article discusses the technological progress toward self-driving vehicles and the potential capability for an autonomous energy supply enabled by dynamic on-road wireless power transfer .
Both scientist and companies have paid attention to electric vehicles ( EVs ) as a clean energy to reduce the oil demands and gas emissions . On the other hand , autonomous electric vehicles have been seen as cutting-edge technology . Self-driving vehicles , and vehicle ride-sharing offered by autonomous EVs are transportation services that have surged quickly .
In this paper , we presented a number of empirical evaluations of recent deep learning advances . Computer vision , combined with deep learning , has the potential to bring about a relatively inexpensive , robust solution to autonomous driving . To prepare deep learning for industry uptake and practical applications , neural networks will require large data sets that represent all possible driving environments and scenarios .
document : Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes During the last half decade , convolutional neural networks ( CNNs ) have triumphed over semantic segmentation , which is a core task of various emerging industrial applications such as autonomous driving and medical imaging . However , to train CNNs requires a huge amount of data , which is difficult to collect and laborious to annotate .
As crucial information , lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames . The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic , dice similarity coefficient and real-time performance . For lane departure detection , a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform .
Digital libraries : Meeting place for high-level and low-level vision.- Computer vision in next generation image and video coding.- Video annotation : Computers watching video.- Video representations.- Analysis of left ventricular motion.- Applications of computer vision in space robotics.- Optical-flow estimation while preserving its discontinuities : A variational approach.- Space- and time-variant estimation approaches and the segmentation of the resulting optical flow fields.- Combining the hough transform and multiresolution MRF 's for the robust motion estimation.- Direct model-based image motion segmentation for dynamic scene analysis.- Modeling structured environments using robot vision.- Design trade-offs for real-time block-matching motion estimation algorithms.- Pixel matching and motion segmentation in image sequences.- On the motion problem of a rigid planar patch and its invariance.- Virtual space teleconferencing : Real-time detection and reproduction of 3D face and body images.- High-performance tracking with TRICLOPS.- Developing an active observer.- Active stereo vision system with foveated wide angle lenses.- A control theoretical approach to active vision.- Active rangefinding and recognition with Cubicscope.- Real-time vision is not frame-rate image analysis.- Active vision based stereo vision.- Motion of a stereo rig : Strong , weak and self calibration.- Color stereo vision : Use of appearance constraint and epipolar geometry for feature matching.- Unifying stereo , motion and object recognition via epipolar geometry.- Constructing a visual memory in terms of uniqueness.- Statistical models & algorithms for tomography.- Shape statistics and image analysis.- Cluster Monte Carlo algorithms and their applications.- Gibbs sampler and maximum likelihood estimation for unsupervised image segmentations.- Lane boundary detection using deformable templates : Effects of image subsampling on detected lane edges.- Clique finding relaxation labeling networks.- Rectifying structural matching errors.- Parallel distributed relaxation labeling.- Subgraph isomorphism detection in polynomial time on preprocessed model graphs.- Line postprocessing using probabilistic relaxation and relative measurements.- Multiple-view geometry and photometry.- Euclidean 3D reconstruction from stereo sequences with variable focal lengths.- Characterizing the stability of 3D invariants derived from 3D translational symmetry.- MORSE : An architecture for 3D object recognition based on invariants.- Finding human faces with a Gaussian mixture distribution-based face model.- Handwriting recognition.- An integrated model for evaluating the amount of data required for reliable recognition.- 3D object recognition using bidirectional modular networks.- A region extension method for natural outdoor scene interpretation.- Image processing techniques for crowd density estimation using a reference image.- Autonomous road vehicle guidance in normal traffic.- Evaluation of image sequences from outdoor scenes : Selected problems and solutions.- Vehicle localisation and discrimination in outdoor traffic scenes.- SWIM : A prototype environment for visual media retrieval.- Video segment indexing through classification and interactive view-based query.- View-based techniques for searching for objects and textures.- Graph grammar based object recognition for image retrieval.- Similarity matching.- A document image analysis and recognition system for Japanese family registration.- Applying handwriting recognition to US census forms .
In this paper , a robust lane detection approach , that is primary and essential for driver assistance systems , is proposed to handle the situations where the lane boundaries in an image have relatively weak local contrast , or where there are strong distracting edges . The proposed lane detection approach makes use of a deformable template model to the expected lane boundaries in the image , a maximum a posteriori ( MAP ) formulation of the lane detection problem , and a particle swarm optimization algorithm to maximize the posterior density . The model parameters completely determine the position of the vehicle inside the lane , its heading direction , and the local structure of the lane .
Because this method uses SVM , which has a better classified performance than BP(Back Propagation)neural network , it obtains a better detection result than that using BP neural network(H.Jeong , et al).Furthermore , this method uses PCNN to process the frames to remove the shadow in the road , reducing the effect of illumination variations . Experimental results show that this method can receive better lane detecting results than the trapezoidal model and BP neural network proposed by H.Jeong , et al.
Furthermore , in preprocessing using PCNN removes the shadow in the road to reduce the effect of illumination variations . Experimental results show that our method can receive better lane detecting results than the trapezoidal model and BP proposed by H. Jeong , et al ..
Due to the complexity of urban traffic scenes , false lane detections are highly caused by warning lines and signs whose shapes and colors are similar to the lane boundary . In this study , we improve the accuracy of the lane detection base on Hough , a score function based on the width between left and right lanes is proposed to obtain reliable lane detect results on urban traffic scene . Meanwhile , a list of candidate lanes is constructed at the least of execution time .
In this paper , we propose a real-time multi-lane detection algorithm that is robust to the challenging lane condition . For basic lane detection part , we extract the lane features from images , and based on the features geometric uncertainty algorithm we can detect the lanes , guardrails , other vehicles in real-time . What ’ s more , the accuracy can be improved with the other vehicles direction information and guardrail depth information from stereo vision when the lane features are non-continuous or poor .
The following paper deals with a lane centering system for motorways , which is implemented into a demonstrator vehicle in order to evaluate sensor performance and support the development process of assistance systems for lateral vehicle dynamics and lane detection sensors . The implemented system uses lane information acquired by a mono camera system , which is mounted behind the windshield of the demonstrator vehicle .
In order to improve adaptability to different perspective conditions , a width-adaptive lane detection method is proposed . As a useful reference to reduce noises , vanishing point is widely applied in lane detection studies . However , vanishing point detection based on original image consumes many calculation resources .
The effective detected region was selected with the combination of the regional contrast analysis and OTSU method , and then the image was segmented with adaptive threshold . The experimental result of all kinds of typical lane image detection shows that not only the real time request can be satisfied but also the adaptive capability and the robustness to the influence of discontinuous lane marking can be increased .
To prepare deep learning for industry uptake and practical applications , neural networks will require large data sets that represent all possible driving environments and scenarios . We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection . We show how existing convolutional neural networks ( CNNs ) can be used to perform lane and vehicle detection while running at frame rates required for a real - time system . Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving .
In this paper , we detail empirical evaluation on the data set we collect . In addition , we explain the neural network that we applied for detecting lanes and cars , as shown in Figure [ reference ] . section : Related Work
This allows for the feature vector to cover the full stride size of pixels ; the end result is a grid mask detector of size where each element is pixels which covers the entire input image of size . subsection : Lane Detection The CNN used for vehicle detection can be easily extended for lane boundary detection by adding an additional class .
The power assist suit is supposed to support the wearer so that it reduces the load of the radiation protection equipment during an operation . This paper proposes a power assist control method based on motion prediction using 9-axis motion sensors . The power assist suit enables rapid power assist because the motion sensor can detect the start of walking motion in real time .
The model captured the behavioral decrease in tracking accuracy with both increasing trial duration ( exp 1 ; p < .001 ) and increasing disc speed ( exp 2 ; p < .001 ) , although predicted accuracy in exp 2 was slightly higher than what was found in the behavioral data ( p = .05 ) . We conclude that motion prediction , simulated here by simple Kalman filters , can describe tracking accuracy in two widely-studied MOT tasks . Meeting abstract presented at VSS 2015 .
We use 3D object tracking to mine for more than 300k interesting vehicle trajectories to create a trajectory forecasting benchmark . Motion forecasting experiments ranging in complexity from classical methods ( k-NN ) to LSTMs demonstrate that using detailed vector maps with lane-level information substantially reduces prediction error . Our tracking and forecasting experiments represent only a superficial exploration of the potential of rich maps in robotic perception .
In this work , we present LaserFlow , an efficient method for 3D object detection and motion forecasting from LiDAR . Unlike the previous work , our approach utilizes the native range view representation of the LiDAR , which enables our method to operate at the full range of the sensor in real-time without voxelization or compression of the data .
This new kind of systems generates not only new possibilities but also new problems to be solved . Among them are real-time rendering , motion estimation and prediction , animation techniques like autonomous actors , new three-dimensional interaction techniques , and architectures that embody clear concepts and software engineering methodologies for such kind of systems . We analyze a number of existing systems that illustrate what we believe are core concepts and difficulties to be overcome .
In Fig. 1 we show the tracking maneuver of a freshwater turtle as it strives to capture a moving fish . Turtles anticipate future position of a moving target by solving a motion prediction problem – a task that is believed to be initiated in the visual cortex . Visual inputs to the retina are routed through the geniculate before it hits the cortex ( see Fig. 2 ) .
This paper addresses the motion prediction problem of a planetary vehicle under the following hypotheses : the vehicle must be able to move on both , natural and hostile terrains , it must have the ability of selecting and applying several navigation strategies depending on the characteristics of the terrain , and it must be capable of showing complex behaviors derived by both , its sophisticated mechanical structure and the vehicle/terrain physical interactions . An original method to simulate the dynamic behavior of the vehicle ( i.e. , vehicle/terrain interactions , sliding or gripping effects ... ) when executing motions having the previous characteristics is presented .
Contemporary situational awareness problems such as automated normalcy learning for anomaly detection and motion behavior prediction are addressed with biologically-inspired processing , representation , and learning approaches . Issues and challenges are discussed and our responses to them described .
It predicts two most important vehicle motion parameters ( forward and angular velocity ) . For this , we have adapted an existing architecture ( proposed by Zhang et al. ) and improved motion parameters prediction . A video dataset has been collected using android smartphone ( Pilotguru ) as capturing rash instances was necessary .
This paper investigates a novel approach , target motion modeling through the grey prediction model , to compensate the uncertainty with the correction action of the robot . Firstly , with consideration of the nonlinear and non-gaussian characteristics of the environment and target tracking , the particle filter algorithm is introduced for motion prediction and estimation , and the target position in the robot vision can be calculated . Then , an intention inference based reinforcement learning control method is used to estimate the mapping from sensory information to appropriate robot action based on the characteristics of the method , which , do not need the model of the environment and the robot .
Aiming at the local optimal problem caused by precocious particles in the path planning process of mobile robot by traditional particle swarm optimization algorithm ( PSO ) , a hybrid neural process-pso algorithm was constructed by integrating the idea of motion process prediction into PSO . The main idea is that in the next iteration of particle swarm individuals , the neural process is used to predict the individual location , increasing the diversity of particle swarm at the later stage of the iteration , and avoiding falling into local optimization too early , so as to improve the optimization ability of the algorithm .
The proposed method is able to generate smooth human motions that resemble the original motion from the same viewpoint the sequence was taken , and look continuous from any other viewpoint . The core of the proposed system is the motion trend prediction for reconstruction . To focus the research effort on motion reconstruction , “ synthesized ” input is first employed to ensure that the reconstruction algorithm is developed and evaluated accurately .
paragraph : Image alignment Image alignment is a key step in many computer vision tasks , including face verification , motion analysis , stereo matching , and object recognition . Alignment results in correspondence across different images by removing intraclass variability and canonicalizing pose .
Spatio - temporal filters . Burt and Adelson lay out the theory of spatio - temporal models for motion estimation and Heeger provides a computational embodiment . While inspired by human perception , such methods did not perform well at the time .
The chosen shape recognition algorithm is the Histogram of Oriented Gradients . This method has been proved to be capable to recognize complex objects and is widely used for pedestrian recognition . The complex objects we recognize are other humanoid robots ( in particular Naos ) , similar in complexity for their anthropomorphic shape but less variable than human beings .
However , they can not detect crossing pedestrians when turning at intersections because the field of view is not wide enough . Thus , we propose to utilize a surround view camera system becoming popular by making it into stereo vision which is robust for the pedestrian recognition . However , conventional stereo camera technologies can not be applied due to fisheye cameras and uncalibrated camera poses .
As a result of its important role in video surveillance , pedestrian attribute recognition has become an attractive facet of computer vision research . Because of the changes in viewpoints , illumination , resolution and occlusion , the task is very challenging .
In surveillance scenarios , the pedestrian images are often facing poor resolution problems or the images are often suffered the occlusion problems . These problems make pedestrian attribute recognition more difficult . In order to solve this problem , we propose an improved pedestrian attribute recognition method based on hand-crafted feature .
Pedestrian analysis plays a vital role in intelligent video surveillance and security-centric computer vision systems . Despite that the deep convolutional neural networks ( DCNNs ) achieved remarkable performance in computer vision , learning fine-grained features of pedestrian attribute tasks is a challenging task in complex surveillance scenarios . In this paper , we proposed a new DCNNs framework for pedestrian analysis , with the main idea of integrating different learning tasks of pedestrian body parts detection and pedestrian attribute classification , which called Hyper-pedestrian convolutional neural network ( HP-CNN ) . Additionally , the proposed HP-CNN bring some advantages : 1 ) Squeeze-and-excitation block ( SE-block ) strengthens the representational power of networks by selectively emphasising informative features ; 2 ) Multi-scale feature fusion concatenates more fine-grained information from both the low-level and high-level and enhances the contextual information from different convolutional layers .
Experiments show that the proposed method significantly outperforms the SVM based method on the PETA database . Multi-label convolutional neural network for pedestrian attribute classification . The proposed MLCNN can simultaneously predict multiple pedestrian attributes .
Our MTNet is an end-to-end deep learning framework , which all the parameters and losses can be jointly optimized . In our method we combine two tasks closely corresponding to person re-identification , pedestrian identity task and pedestrian attribute task , who provide complementary information from different perspective by integrating multi-context informations . Attribute focuses on some special aspects of a person , while identity pays more attention to overall contour and appearance .
Pedestrian Attribute Recognition ( PAR ) is an important task in surveillance video analysis . In this paper , we propose a novel end-to-end hierarchical deep learning approach to PAR .
Extracting visual attributes from a scene , such as the type of shoes , the type of clothing , carrying any object or not , or wearing any accessory etc . , is a challenging problem and an efficient solution holds the key to a great number of applications . In this paper , we present a multi-branch convolutional neural network that uses depthwise separable convolution ( DSC ) layers to solve the pedestrian attribute recognition problem . Researchers have proposed various solutions over the years making use of convolutional neural networks ( CNN ) , however , we introduce DSC layers to the CNN for the problem of pedestrian attribute recognition .
This paper expands the strength of deep convolutional neural networks ( CNNs ) to the pedestrian attribute recognition problem by devising a novel attribute aware pooling algorithm . Existing vanilla CNNs can not be straightforwardly applied to handle multi-attribute data because of the larger label space as well as the attribute entanglement and correlations .
Pedestrian detection and abnormal behavior detection is the computer for a given image and video , to determine whether there are pedestrians and their behavior is normal . Pedestrian detection is the basis and premise of pedestrian tracking , behavior analysis , gait analysis , pedestrian identity recognition and so on . A good pedestrian detection algorithm can provide strong support and guarantee for the latter .
This joint learning framework is named Joint Learning of Parsing attention , Label attention , and Spatial attention for Pedestrian Attributes Analysis ( JLPLS-PAA , for short ) . Extensive comparative evaluations conducted on multiple large-scale benchmarks , including PA-100 K , RAP , PETA , Market-1501 , and Duke attribute datasets , further demonstrate the effectiveness of the proposed JLPLS-PAA framework for pedestrian attribute analysis .
The capability for recognizing pedestrian semantic attributes , such as gender , clothes color and other semantic attributes is of practical significance in bank smart surveillance , intelligent transportation and so on . In order to recognize the key multi attributes of pedestrians in indoor and outdoor scenes , this paper proposes a deep network with dynamic weights and joint loss function for pedestrian key attribute recognition . First , a new multi-label and multi-attribute pedestrian dataset , which is named NEU-dataset , is built .
document : Small - scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation A critical issue in pedestrian detection is to detect small - scale objects that will introduce feeble contrast and motion blur in images and videos , which in our opinion should partially resort to deep - rooted annotation bias . Motivated by this , we propose a novel method integrated with somatic topological line localization ( TLL ) and temporal feature aggregation for detecting multi - scale pedestrians , which works particularly well with small - scale pedestrians that are relatively far from the camera .
section : Introduction Pedestrian detection is a critical problem in computer vision with significant impact on a number of applications , such as urban autonomous driving , surveillance and robotics . In recent years many works have been devoted to this detection task , however , there still leaves a critical bottleneck caused by various scales of pedestrians in an image .
‘ R - CNN with AlexNet ’ ’ with the distinction made clear by the context . During R - CNN training AlexNet is fine - tuned for the ( pedestrian ) detection task , and in a second step , the softmax output is replaced by a linear SVM . Unless otherwise specified , we use the default parameters of the open source , Caffe based , R - CNN implementation .
However , these fine - grained attributes are very useful to distinguish the pedestrian pairs with small inter - class variations . Thus these DCNN models are not the best choice for pedestrian feature learning . Second , due to the pose variations and imperfect pedestrian detectors , the pedestrian image samples may be misaligned .
We further evaluate the approach extensively for an image classification task , and present results on traditional deep learning image data sets - such as MNIST [ reference ] and SVHN [ reference ])- as well as on Office benchmarks [ reference ] , where domain - adversarial learning allows obtaining a deep architecture that considerably improves over previous state - of - the - art accuracy . Finally , we evaluate domain - adversarial descriptor learning in the context of person re - identification application [ reference ] , where the task is to obtain good pedestrian image descriptors that are suitable for retrieval and verification . We apply domainadversarial learning , as we consider a descriptor predictor trained with a Siamese - like loss instead of the label predictor trained with a classification loss .
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GLOBECOM ITSC CPS-SPC@CCS ICOIN ArXiv 2017 IEEE International Conference on Computer Vision (ICCV) CIS (2) ArXiv CEC CVPR EUROSIM Modeling and Control of Complex Systems IROS FUSION ICVGIP NIPS 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ITSC ICME Workshops ACM Multimedia IJCAI ArXiv 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) J. Mach. Learn. Res.
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