The special steering characteristics and task complexity of autonomous articulated vehicle ( AAV ) make it often require multiple forward and backward movements during autonomous driving . In this paper , we present a simple yet effective method , named head correction with fixed wheel position ( HC-FWP ) , for the demand of multiple forward and backward movements . We suggest that a ` vision front end ' processor , or VFE , will be a key element of practical vision systems that perform complex tasks in real time , such as autonomous vehicle driving . The VFE is a specialized image processing device located between the cameras and the main vision computer . We first introduce the key characteristics of MLS systems and the corresponding point clouds , and present the challenges and opportunities of using the data . Next , we summarize the current applications of using MLS over urban areas , including transportation infrastructure mapping , building information modeling , utility surveying and mapping , vegetation inventory , and autonomous vehicle driving . Then , we review common key issues for processing and analyzing MLS point clouds , including classification methods , object recognition , data registration , data fusion , and 3D city modeling . 3-D data obtained from stereo images form the basis for many higher level tasks like robot navigation , object recognition , 3-D modeling , path planning etc . in the field of interplanetary mission , hazardous environments , deep sea exploration , autonomous car driving . In this work , we attempt to recover the 3-D information from the stereo images for developing the vision based navigation of future planetary rover missions . In this paper , a set of design principles are elaborated as an extension of the previous work , including robust perception and cognition algorithms for environment monitoring and high level decision making and low level control algorithms for safe maneuvering of the automated vehicle . The autonomous driving problem in mixed traffic is posed as a stochastic optimization problem , which is solved by 1 ) behavior classification and trajectory prediction of other road participants , and 2 ) a unique parallel planner architecture which addresses the efficiency goal in the long term and the safety goal in the short term separately . Moreover , a pythonbased high fidelity simulation system is developed and extensive simulations are performed to evaluate the effectiveness of the proposed algorithm , where both high level decision making and low level vehicle regulation are considered . The model is trained and evaluated using the comma.ai dataset , which contains the front view image frames and the steering angle data captured when driving on the road . Unlike the traditional approach that manually decomposes the autonomous driving problem into technical components such as lane detection , path planning and steering control , the end-to-end model can directly steer the vehicle from the front view camera data after training . It learns how to keep in lane from human driving data . We respond to this important and substantive criticism by performing a computation required by the NIM on an example problem . We have chosen autonomous on-road driving , a problem that has already been solved by " systems " that are known to be both complex and specified , namely , humans . We will begin with a concise statement of the scope of the problem and a summary description of an appropriate system representation approach . We present a motion planning framework for autonomous on-road driving considering both the uncertainty caused by an autonomous vehicle and other traffic participants . The future motion of traffic participants is predicted using a local planner , and the uncertainty along the predicted trajectory is computed based on Gaussian propagation . Through modern driver assistant systems , algorithmic decisions already have a significant impact on the behavior of vehicles in everyday traffic . This will become even more prominent in the near future considering the development of autonomous driving functionality . The need to consider ethical principles in the design of such systems is generally acknowledged . Car manufacturers spend quite a lot on the development of driver assistance systems and subsequently on autonomous driving functionality . To ensure the safety and reliability of these functions meet industrial standards it is necessary to verify and validate their functionality . This paper presents a cost-based adaptive planning agent that is operating at the route-segment level of a deliberative hierarchical planning system for autonomous road driving . At this level , the planning agent is responsible for developing fundamental driving maneuvers that allow a vehicle to travel safely amongst moving and stationary objects . This study suggested a boarding robot design in consideration of the old and the infirm and a method for the boarding robot to drive autonomously under in and outdoor environment . This study , to improve the life quality of the old and the infirm who are hard to get about independently , aims at development of a boarding type mobility assisting robot platform capable of autonomic driving and equipped with manual operation functions to prevent collision or crash caused by unskilled operation . Consequently , even though embedded software has become the leading innovation factor in automotive applications , many highly safety-critical automotive functionalities are only reluctantly implemented with software-based solutions . Here , by advancing the applicability and scalability of these advanced technologies and providing support in form of qualified tool chains , a substantial change in the development of automotive software can be achieved , allowing not only to virtualize and thus substitute physical solutions of automotive functions ( e.g. , X-by-wire solutions ) , but also to implement a new range of functionalities ( e.g. , autonomic driving ) . Current trends in the automotive industry are increasingly focusing on the concept of autonomous/ automated driving . At the heart of this concept is the need to provide new solutions dedicated to increase safety in operation , reduce the risk of accidents and make automotive systems more efficient . Vehicular Ad-hoc Networks ( VANETs ) are envisioned to offer support for a large variety of distributed applications that range from alerting drivers to autonomous driving features and video services . The use of video-equipped vehicles , with support for live transmission , unveils the need for an adaptive Quality of Experience (QoE)-driven mechanism to overcome several challenges and provide a good video quality . section : Introduction Recently , natural scene text detection has attracted extensive attention for its numerous applications , such as scene understanding , product identification , automatic driving and target geolocation . However , due to the large variations in foreground texts and background objects , and the diverse text variabilities in shapes , colors , fonts , orientations and scales , along with the extreme illumination and occlusion , text detection in natural scene is still faced with considerable challenges . Humans glance at an image and instantly know what objects are in the image , where they are , and how they interact . The human visual system is fast and accurate , allowing us to perform complex tasks like driving with little conscious thought . Fast , accurate algorithms for object detection would allow computers to drive cars without specialized sensors , enable assistive devices to convey real - time scene information to human users , and unlock the potential for general purpose , responsive robotic systems . The trained network is rather supposed to be generic and to work well in various scenarios . An additional optional component in dataset schedules is fine - tuning of a generic network to a specific scenario , such as the driving scenario , which we show in Section [ reference ] . section : Stacking Networks We also estimate the variance in the outputs of the large network and propose a loss function to incorporate this uncertainty . Finally , we attempt to boost the complexity of the small network without increasing its size by using as input hand-designed features that have been demonstrated to be effective for pedestrian detection . We succeed in training a model that contains $ 400\times$ fewer parameters than the large network while outperforming AlexNet on the Caltech Pedestrian Dataset . We demonstrate that the proposed approach is able to boost the detection performance of state-of-the-art non-deep pedestrian detectors . We apply the proposed methodology to address the pedestrian detection problem on the publicly available datasets INRIA and Caltech . Using the privileged information classification to solve the challenge between the speed and the quality in pedestrian detection is a new direction of research in the compute vision smca . In this paper , we apply Histogram Intersection Kernel ( HIK ) into Learning model Using Privileged Information (LUPI)to improve the quality of pedestrian detection problem . All experimental results show the robustness and effectiveness of the proposed method . The method is able to reduce the confidence of the false positives due to the lack of spatial consensus when multiple detectors are considered . Our experimental validation , performed on three pedestrian detection benchmarks , INRIA person , ETH and Caltech pedestrian dataset , demonstrates that the proposed approach , referred to as Spatial Consensus ( SC ) , outperforms the state-of-the-art on INRIA and ETH datasets and achieves comparable results on the Caltech dataset . One solution is to simplify this working flow as a single-stage detector . However , current single-stage detectors ( e.g. SSD ) have not presented competitive accuracy on common pedestrian detection benchmarks . This paper is towards a successful pedestrian detector enjoying the speed of SSD while maintaining the accuracy of Faster R-CNN . Nevertheless , a major bottleneck of existing state-of-the-art approaches lies in a great drop in performance with reducing resolutions of the detected targets . For the boosting-based detectors which are popular in pedestrian detection literature , a possible cause for this drop is that in their boosting training process , low-resolution samples , which are usually more difficult to be detected due to the missing details , are still treated equally importantly as high-resolution samples , resulting in the false negatives since they are more easily rejected in the early stages and can hardly be recovered in the late stages . To address this problem , we propose in this paper a robust multi-resolution detection approach with a novel group cost-sensitive boosting algorithm , which is derived from the standard AdaBoost algorithm to further explore different costs for different resolution groups of the samples in the boosting process , and to place greater emphasis on low-resolution groups in order to better handle the detection of multi-resolution targets . We evaluate the Aggregate Channel Features ( ACF ) and Deformable Parts Model ( DPM ) algorithms from the literature , as well as our own implementation of a Convolutional Neural Network ( CNN ) . We show that the traditional performance metric used in the pedestrian detection literature is extremely sensitive to parameterization . When applied in domains like this one , where localization is challenging due to high background texture and occlusion , the choice of overlap threshold strongly affects measured performance . To overcome this problem , employing special hardware for artificial intelligence has been studied . In this paper , we have implemented a pedestrian image detection system on an embedded device using NM500 neuromorphic chip . One NM500 chip contains 576 neurons , and we have measured the accuracy and power consumption , increasing the number of chips from one to seven . Our strategy boosts the k-NN classification performance ; considering heterogeneous metrics and analyzing 15 diverse datasets , we are among the best 6 prototype-based k-NN approaches , with a computational cost which is strongly inferior to all the competitors . In addition , we show that our proposal beats linear SVM in the case of a pedestrian detection scenario . We face this issue , proposing a computational framework which is based on an expressive descriptor , the covariance of features . Covariances have been employed for pedestrian detection purposes , actually a binary classification problem on Riemannian manifolds . In this paper , we show how to extend to the multiclassification case , presenting a novel descriptor , named weighted array of covariances , especially suited for dealing with tiny image representations . This article contains results of data processing on pedestrian detection test . Pedestrian detection is herein implemented using the automotive MIMO radar . It is considered a canonical case of object detection , and has served as playground to explore ideas that might be effective for generic object detection . Although many different ideas have been explored , and detection quality has been steadily improving , arguably it is still unclear what are the key ingredients for good pedestrian detection ; e.g. it remains unclear how effective parts , components , and features learning are for this task . Current top performing pedestrian detection methods all point to an intermediate layer ( such as max - pooling or filtering ) between the low - level feature maps and the classification layer . document : Occlusion - aware R - CNN : Detecting Pedestrians in a Crowd Pedestrian detection in crowded scenes is a challenging problem since the pedestrians often gather together and occlude each other . In this paper , we propose a new occlusion - aware R - CNN ( OR - CNN ) to improve the detection accuracy in the crowd . [ ] exploit weakly annotated boxes via a segmentation infusion network to achieve considerable performance gains . However , occlusion still remains one of the most significant challenges in pedestrian detection , which increases the difficulty in pedestrian localization . Several methods use part - based model to describe the pedestrian in occlusion handling , which learn a series of part detectors and design some mechanisms to fuse the part detection results to localize partially occluded pedestrians . section : Conclusions In this paper , we present a new occlusion - aware R - CNN method to improve the pedestrian detection accuracy in crowded scenes . Specifically , we design a new aggregation loss to reduce the false detections of the adjacent overlapping pedestrians , by simultaneously enforcing the proposals to be close to the associated objects , and locate compactly . Meanwhile , to effectively handle partial occlusion , we propose a new part occlusion - aware RoI pooling unit to replace the RoI pooling layer in the Fast R - CNN module of the detector , which integrates the prior structure information of human body with visibility prediction into the network to handle occlusion . Our method is trained in an end - to - end fashion and achieves the state - of - the - art accuracy on three pedestrian detection datasets , i.e. , CityPersons , ETH , and INRIA , and performs on - pair with the state - of - the - arts on Caltech . In the future , we plan to improve the method in two aspects . document : Scale - aware Fast R - CNN for Pedestrian Detection In this work , we consider the problem of pedestrian detection in natural scenes . We provide an illustration of the motivation of the paper in Figure [ reference ] . Pedestrian instances in the video surveillance images ( e.g. , Caltech dataset ) often have very small sizes . Statistically , over of the instances from the Caltech training set have a height smaller than pixels . Secondly , a scale - aware weighting mechanism is proposed to lift the contribution of the sub - network specialized for the current input scales and boost the final detection performance in a wide input scale range . Thirdly , extensive experiments on several challenging pedestrian datasets demonstrate that SAF R - CNN delivers new state - of - the - art performance on three out of four challenging pedestrian benchmarks . section : Related Work It can be seen that SAF R - CNN significantly outperforms all four baselines . It verifies the superiority of using our scale - aware weighting technique in SAF R - CNN to detect the pedestrian instances with various sizes . We also compare the testing time of SAF R - CNN with other baselines . Note the bounding - box annotation instances are normalized to the same aspect ratio as line annotation ones for fair statistics . The test result emphasizes that line annotation promotes more precise localisation on pedestrian than marking a bounding box , especially for small - scale instances . The reason lies in that annotators tend to align well with the center of subjects when drawing lines . This may cause the TLL network to output very close vertices , and high link scores between each candidate pairs , leading to confused detection results . Thus , the crowd occlusion severely increases the difficulty in pedestrian localization . In order to robustly localize each individual pedestrian in crowd scenes , locations of its surrounding objects need to be taken into account . But we have to increase its parameter setting from 3 scales and 3 aspect ratios in to 10 scales and 7 aspect ratios in order to make it work on KITTI . Finally , our RPN performs on par with Faster R - CNN on car , and outperforms it on pedestrian and cyclist using the same number of proposals per image . Our new architecture can better handle scale variation using image pyramid . paragraph : Model window size A typical choice for pedestrian detectors is a model window of in which the pedestrian occupies an area of . It is unclear that this is the ideal input size for convnets . section : Conclusion In this work , we have studied the problem of person ReID in three levels : 1 ) a multi - scale context - aware network to capture the context knowledge for pedestrian feature learning , 2 ) three novel constraints on STN for effective latent parts localization and body - part feature representation , 3 ) the fusion of full - body and body - part identity discriminative features for powerful pedestrian representation . We have validated the effectiveness of the proposed method on current large - scale person ReID datasets . Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment . However , deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems , but also in generalising previously learned rules to new scenarios . Combined road marking and traffic sign databases are beneficial for both road maintenance and for usage within navigation devices and autonomous driving vehicles . The combination of both markings and signs completely provides all instructions and legislation for drivers . Robust vanishing point detection is an important machine vision task and is widely used in pattern recognition for robotics , advanced driver assistance systems , and autonomous driving vehicles . At present , there are many vanishing point detection algorithms , but it is still difficult to satisfy both the accuracy and real-time requirements . In the past , work at the Universitat der Bundeswehr Muenchen ( UBM ) has been focused on autonomous road vehicles . During the last four years the Expectation-based Multi-focal Saccadic Vision ( EMS-Vision ) system has been developed and implemented . Ultracapacitors are an alternative storage element to deal with the power requirements of autonomous electric vehicles . This is possible because ultracapacitors can deliver large amount of power in short periods of time and their shelf life is longer than other conventional storage elements . There has been much technological advancement in the wireless communic ation in the recent past for the intelligent transportation systems among all Dedicated Short - Range Communication or DSRC or Wireless Access for Vehicular Environment ( WAVE ) has very important role for the autonomous connected vehicles . It is well known fo r its support for the time sensitive safety applications . section : Introduction With the exciting vision of autonomous driving , semantic segmentation of urban scenes , as a key module , has gained increasing attention from both academia and industry . However , collecting and labeling training data for semantic segmentation task is a laborious and expensive process , as it requires per - pixel annotation . They also became much more reliable , with state - of - the - art speech recognition engines reaching human level in English . This achievement unlocked many practical applications of voice assistants which are now used in many fields from customer support , to autonomous cars , or smart homes . In particular , smart speaker adoption by the public is on the rise , with a recent study showing that nearly 20 % of U.S. adults reported having a smart speaker at home . Likewise , the use of drones with autonomous flight is common nowadays . During the drone ’s flight , especially autonomous flight in forest , the blades of the rotor may be damaged , scratched or bent due to a collision with a branch or object . These types of mishap can cause the blade to become unbalanced , causing the drone to oscillate or , in the worst case , fall , causing further damage .
The special steering characteristics and task complexity of autonomous articulated vehicle ( AAV ) make it often require multiple forward and backward movements during autonomous driving . In this paper , we present a simple yet effective method , named head correction with fixed wheel position ( HC-FWP ) , for the demand of multiple forward and backward movements .
We suggest that a ` vision front end ' processor , or VFE , will be a key element of practical vision systems that perform complex tasks in real time , such as autonomous vehicle driving . The VFE is a specialized image processing device located between the cameras and the main vision computer .
We first introduce the key characteristics of MLS systems and the corresponding point clouds , and present the challenges and opportunities of using the data . Next , we summarize the current applications of using MLS over urban areas , including transportation infrastructure mapping , building information modeling , utility surveying and mapping , vegetation inventory , and autonomous vehicle driving . Then , we review common key issues for processing and analyzing MLS point clouds , including classification methods , object recognition , data registration , data fusion , and 3D city modeling .
3-D data obtained from stereo images form the basis for many higher level tasks like robot navigation , object recognition , 3-D modeling , path planning etc . in the field of interplanetary mission , hazardous environments , deep sea exploration , autonomous car driving . In this work , we attempt to recover the 3-D information from the stereo images for developing the vision based navigation of future planetary rover missions .
In this paper , a set of design principles are elaborated as an extension of the previous work , including robust perception and cognition algorithms for environment monitoring and high level decision making and low level control algorithms for safe maneuvering of the automated vehicle . The autonomous driving problem in mixed traffic is posed as a stochastic optimization problem , which is solved by 1 ) behavior classification and trajectory prediction of other road participants , and 2 ) a unique parallel planner architecture which addresses the efficiency goal in the long term and the safety goal in the short term separately . Moreover , a pythonbased high fidelity simulation system is developed and extensive simulations are performed to evaluate the effectiveness of the proposed algorithm , where both high level decision making and low level vehicle regulation are considered .
The model is trained and evaluated using the comma.ai dataset , which contains the front view image frames and the steering angle data captured when driving on the road . Unlike the traditional approach that manually decomposes the autonomous driving problem into technical components such as lane detection , path planning and steering control , the end-to-end model can directly steer the vehicle from the front view camera data after training . It learns how to keep in lane from human driving data .
We respond to this important and substantive criticism by performing a computation required by the NIM on an example problem . We have chosen autonomous on-road driving , a problem that has already been solved by " systems " that are known to be both complex and specified , namely , humans . We will begin with a concise statement of the scope of the problem and a summary description of an appropriate system representation approach .
We present a motion planning framework for autonomous on-road driving considering both the uncertainty caused by an autonomous vehicle and other traffic participants . The future motion of traffic participants is predicted using a local planner , and the uncertainty along the predicted trajectory is computed based on Gaussian propagation .
Through modern driver assistant systems , algorithmic decisions already have a significant impact on the behavior of vehicles in everyday traffic . This will become even more prominent in the near future considering the development of autonomous driving functionality . The need to consider ethical principles in the design of such systems is generally acknowledged .
Car manufacturers spend quite a lot on the development of driver assistance systems and subsequently on autonomous driving functionality . To ensure the safety and reliability of these functions meet industrial standards it is necessary to verify and validate their functionality .
This paper presents a cost-based adaptive planning agent that is operating at the route-segment level of a deliberative hierarchical planning system for autonomous road driving . At this level , the planning agent is responsible for developing fundamental driving maneuvers that allow a vehicle to travel safely amongst moving and stationary objects .
This study suggested a boarding robot design in consideration of the old and the infirm and a method for the boarding robot to drive autonomously under in and outdoor environment . This study , to improve the life quality of the old and the infirm who are hard to get about independently , aims at development of a boarding type mobility assisting robot platform capable of autonomic driving and equipped with manual operation functions to prevent collision or crash caused by unskilled operation .
Consequently , even though embedded software has become the leading innovation factor in automotive applications , many highly safety-critical automotive functionalities are only reluctantly implemented with software-based solutions . Here , by advancing the applicability and scalability of these advanced technologies and providing support in form of qualified tool chains , a substantial change in the development of automotive software can be achieved , allowing not only to virtualize and thus substitute physical solutions of automotive functions ( e.g. , X-by-wire solutions ) , but also to implement a new range of functionalities ( e.g. , autonomic driving ) .
Current trends in the automotive industry are increasingly focusing on the concept of autonomous/ automated driving . At the heart of this concept is the need to provide new solutions dedicated to increase safety in operation , reduce the risk of accidents and make automotive systems more efficient .
Vehicular Ad-hoc Networks ( VANETs ) are envisioned to offer support for a large variety of distributed applications that range from alerting drivers to autonomous driving features and video services . The use of video-equipped vehicles , with support for live transmission , unveils the need for an adaptive Quality of Experience (QoE)-driven mechanism to overcome several challenges and provide a good video quality .
section : Introduction Recently , natural scene text detection has attracted extensive attention for its numerous applications , such as scene understanding , product identification , automatic driving and target geolocation . However , due to the large variations in foreground texts and background objects , and the diverse text variabilities in shapes , colors , fonts , orientations and scales , along with the extreme illumination and occlusion , text detection in natural scene is still faced with considerable challenges .
Humans glance at an image and instantly know what objects are in the image , where they are , and how they interact . The human visual system is fast and accurate , allowing us to perform complex tasks like driving with little conscious thought . Fast , accurate algorithms for object detection would allow computers to drive cars without specialized sensors , enable assistive devices to convey real - time scene information to human users , and unlock the potential for general purpose , responsive robotic systems .
The trained network is rather supposed to be generic and to work well in various scenarios . An additional optional component in dataset schedules is fine - tuning of a generic network to a specific scenario , such as the driving scenario , which we show in Section [ reference ] . section : Stacking Networks
We also estimate the variance in the outputs of the large network and propose a loss function to incorporate this uncertainty . Finally , we attempt to boost the complexity of the small network without increasing its size by using as input hand-designed features that have been demonstrated to be effective for pedestrian detection . We succeed in training a model that contains $ 400\times$ fewer parameters than the large network while outperforming AlexNet on the Caltech Pedestrian Dataset .
We demonstrate that the proposed approach is able to boost the detection performance of state-of-the-art non-deep pedestrian detectors . We apply the proposed methodology to address the pedestrian detection problem on the publicly available datasets INRIA and Caltech .
Using the privileged information classification to solve the challenge between the speed and the quality in pedestrian detection is a new direction of research in the compute vision smca . In this paper , we apply Histogram Intersection Kernel ( HIK ) into Learning model Using Privileged Information (LUPI)to improve the quality of pedestrian detection problem . All experimental results show the robustness and effectiveness of the proposed method .
The method is able to reduce the confidence of the false positives due to the lack of spatial consensus when multiple detectors are considered . Our experimental validation , performed on three pedestrian detection benchmarks , INRIA person , ETH and Caltech pedestrian dataset , demonstrates that the proposed approach , referred to as Spatial Consensus ( SC ) , outperforms the state-of-the-art on INRIA and ETH datasets and achieves comparable results on the Caltech dataset .
One solution is to simplify this working flow as a single-stage detector . However , current single-stage detectors ( e.g. SSD ) have not presented competitive accuracy on common pedestrian detection benchmarks . This paper is towards a successful pedestrian detector enjoying the speed of SSD while maintaining the accuracy of Faster R-CNN .
Nevertheless , a major bottleneck of existing state-of-the-art approaches lies in a great drop in performance with reducing resolutions of the detected targets . For the boosting-based detectors which are popular in pedestrian detection literature , a possible cause for this drop is that in their boosting training process , low-resolution samples , which are usually more difficult to be detected due to the missing details , are still treated equally importantly as high-resolution samples , resulting in the false negatives since they are more easily rejected in the early stages and can hardly be recovered in the late stages . To address this problem , we propose in this paper a robust multi-resolution detection approach with a novel group cost-sensitive boosting algorithm , which is derived from the standard AdaBoost algorithm to further explore different costs for different resolution groups of the samples in the boosting process , and to place greater emphasis on low-resolution groups in order to better handle the detection of multi-resolution targets .
We evaluate the Aggregate Channel Features ( ACF ) and Deformable Parts Model ( DPM ) algorithms from the literature , as well as our own implementation of a Convolutional Neural Network ( CNN ) . We show that the traditional performance metric used in the pedestrian detection literature is extremely sensitive to parameterization . When applied in domains like this one , where localization is challenging due to high background texture and occlusion , the choice of overlap threshold strongly affects measured performance .
To overcome this problem , employing special hardware for artificial intelligence has been studied . In this paper , we have implemented a pedestrian image detection system on an embedded device using NM500 neuromorphic chip . One NM500 chip contains 576 neurons , and we have measured the accuracy and power consumption , increasing the number of chips from one to seven .
Our strategy boosts the k-NN classification performance ; considering heterogeneous metrics and analyzing 15 diverse datasets , we are among the best 6 prototype-based k-NN approaches , with a computational cost which is strongly inferior to all the competitors . In addition , we show that our proposal beats linear SVM in the case of a pedestrian detection scenario .
We face this issue , proposing a computational framework which is based on an expressive descriptor , the covariance of features . Covariances have been employed for pedestrian detection purposes , actually a binary classification problem on Riemannian manifolds . In this paper , we show how to extend to the multiclassification case , presenting a novel descriptor , named weighted array of covariances , especially suited for dealing with tiny image representations .
This article contains results of data processing on pedestrian detection test . Pedestrian detection is herein implemented using the automotive MIMO radar .
It is considered a canonical case of object detection , and has served as playground to explore ideas that might be effective for generic object detection . Although many different ideas have been explored , and detection quality has been steadily improving , arguably it is still unclear what are the key ingredients for good pedestrian detection ; e.g. it remains unclear how effective parts , components , and features learning are for this task . Current top performing pedestrian detection methods all point to an intermediate layer ( such as max - pooling or filtering ) between the low - level feature maps and the classification layer .
document : Occlusion - aware R - CNN : Detecting Pedestrians in a Crowd Pedestrian detection in crowded scenes is a challenging problem since the pedestrians often gather together and occlude each other . In this paper , we propose a new occlusion - aware R - CNN ( OR - CNN ) to improve the detection accuracy in the crowd .
[ ] exploit weakly annotated boxes via a segmentation infusion network to achieve considerable performance gains . However , occlusion still remains one of the most significant challenges in pedestrian detection , which increases the difficulty in pedestrian localization . Several methods use part - based model to describe the pedestrian in occlusion handling , which learn a series of part detectors and design some mechanisms to fuse the part detection results to localize partially occluded pedestrians .
section : Conclusions In this paper , we present a new occlusion - aware R - CNN method to improve the pedestrian detection accuracy in crowded scenes . Specifically , we design a new aggregation loss to reduce the false detections of the adjacent overlapping pedestrians , by simultaneously enforcing the proposals to be close to the associated objects , and locate compactly . Meanwhile , to effectively handle partial occlusion , we propose a new part occlusion - aware RoI pooling unit to replace the RoI pooling layer in the Fast R - CNN module of the detector , which integrates the prior structure information of human body with visibility prediction into the network to handle occlusion . Our method is trained in an end - to - end fashion and achieves the state - of - the - art accuracy on three pedestrian detection datasets , i.e. , CityPersons , ETH , and INRIA , and performs on - pair with the state - of - the - arts on Caltech . In the future , we plan to improve the method in two aspects .
document : Scale - aware Fast R - CNN for Pedestrian Detection In this work , we consider the problem of pedestrian detection in natural scenes .
We provide an illustration of the motivation of the paper in Figure [ reference ] . Pedestrian instances in the video surveillance images ( e.g. , Caltech dataset ) often have very small sizes . Statistically , over of the instances from the Caltech training set have a height smaller than pixels .
Secondly , a scale - aware weighting mechanism is proposed to lift the contribution of the sub - network specialized for the current input scales and boost the final detection performance in a wide input scale range . Thirdly , extensive experiments on several challenging pedestrian datasets demonstrate that SAF R - CNN delivers new state - of - the - art performance on three out of four challenging pedestrian benchmarks . section : Related Work
It can be seen that SAF R - CNN significantly outperforms all four baselines . It verifies the superiority of using our scale - aware weighting technique in SAF R - CNN to detect the pedestrian instances with various sizes . We also compare the testing time of SAF R - CNN with other baselines .
Note the bounding - box annotation instances are normalized to the same aspect ratio as line annotation ones for fair statistics . The test result emphasizes that line annotation promotes more precise localisation on pedestrian than marking a bounding box , especially for small - scale instances . The reason lies in that annotators tend to align well with the center of subjects when drawing lines .
This may cause the TLL network to output very close vertices , and high link scores between each candidate pairs , leading to confused detection results . Thus , the crowd occlusion severely increases the difficulty in pedestrian localization . In order to robustly localize each individual pedestrian in crowd scenes , locations of its surrounding objects need to be taken into account .
But we have to increase its parameter setting from 3 scales and 3 aspect ratios in to 10 scales and 7 aspect ratios in order to make it work on KITTI . Finally , our RPN performs on par with Faster R - CNN on car , and outperforms it on pedestrian and cyclist using the same number of proposals per image . Our new architecture can better handle scale variation using image pyramid .
paragraph : Model window size A typical choice for pedestrian detectors is a model window of in which the pedestrian occupies an area of . It is unclear that this is the ideal input size for convnets .
section : Conclusion In this work , we have studied the problem of person ReID in three levels : 1 ) a multi - scale context - aware network to capture the context knowledge for pedestrian feature learning , 2 ) three novel constraints on STN for effective latent parts localization and body - part feature representation , 3 ) the fusion of full - body and body - part identity discriminative features for powerful pedestrian representation . We have validated the effectiveness of the proposed method on current large - scale person ReID datasets .
Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment . However , deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems , but also in generalising previously learned rules to new scenarios .
Combined road marking and traffic sign databases are beneficial for both road maintenance and for usage within navigation devices and autonomous driving vehicles . The combination of both markings and signs completely provides all instructions and legislation for drivers .
Robust vanishing point detection is an important machine vision task and is widely used in pattern recognition for robotics , advanced driver assistance systems , and autonomous driving vehicles . At present , there are many vanishing point detection algorithms , but it is still difficult to satisfy both the accuracy and real-time requirements .
In the past , work at the Universitat der Bundeswehr Muenchen ( UBM ) has been focused on autonomous road vehicles . During the last four years the Expectation-based Multi-focal Saccadic Vision ( EMS-Vision ) system has been developed and implemented .
Ultracapacitors are an alternative storage element to deal with the power requirements of autonomous electric vehicles . This is possible because ultracapacitors can deliver large amount of power in short periods of time and their shelf life is longer than other conventional storage elements .
There has been much technological advancement in the wireless communic ation in the recent past for the intelligent transportation systems among all Dedicated Short - Range Communication or DSRC or Wireless Access for Vehicular Environment ( WAVE ) has very important role for the autonomous connected vehicles . It is well known fo r its support for the time sensitive safety applications .
section : Introduction With the exciting vision of autonomous driving , semantic segmentation of urban scenes , as a key module , has gained increasing attention from both academia and industry . However , collecting and labeling training data for semantic segmentation task is a laborious and expensive process , as it requires per - pixel annotation .
They also became much more reliable , with state - of - the - art speech recognition engines reaching human level in English . This achievement unlocked many practical applications of voice assistants which are now used in many fields from customer support , to autonomous cars , or smart homes . In particular , smart speaker adoption by the public is on the rise , with a recent study showing that nearly 20 % of U.S. adults reported having a smart speaker at home .
Likewise , the use of drones with autonomous flight is common nowadays . During the drone ’s flight , especially autonomous flight in forest , the blades of the rotor may be damaged , scratched or bent due to a collision with a branch or object . These types of mishap can cause the blade to become unbalanced , causing the drone to oscillate or , in the worst case , fall , causing further damage .
Path Planning for Autonomous Articulated Vehicle Based on Improved Goal-Directed Rapid-Exploring Random Tree Electronic front-end processor for active vision A Survey of Mobile Laser Scanning Applications and Key Techniques over Urban Areas 3D information retrieval for visual odometry system of planetary exploration rovers - A stereo vision approach The Robustly-Safe Automated Driving System for Enhanced Active Safety End-to-end learning for lane keeping of self-driving cars Exercising a Native Intelligence Metric on an Autonomous On-Road Driving System Motion planning under uncertainty for on-road autonomous driving Distributive justice as an ethical principle for autonomous vehicle behavior beyond hazard scenarios Virtual reality for automotive radars Cost-based adaptive planning for on-road driving Implementation of assistive robotic vehicle for the elderly Certification of Embedded Software - Impact of ISO DIS 26262 in the Automotive Domain Improved Fault Tolerant Control of a Redundant Actuation System Provided with Dual Stator Six Phases BLDC Motor for Autonomous Driving Applications Adaptive QoE-driven video transmission over Vehicular Ad-hoc Networks Shape Robust Text Detection With Progressive Scale Expansion Network You Only Look Once: Unified, Real-Time Object Detection FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks In Teacher We Trust: Learning Compressed Models for Pedestrian Detection Improving the performance of pedestrian detectors using convolutional learning How to Improve the Quality of Pedestrian Detection Using the Priori Knowledge A late fusion approach to combine multiple pedestrian detectors Learning Efficient Single-Stage Pedestrian Detectors by Asymptotic Localization Fitting Detecting Multi-Resolution Pedestrians Using Group Cost-Sensitive Boosting with Channel Features † People in the weeds: Pedestrian detection goes off-road Power Consumption and Accuracy in Detecting Pedestrian Images on Neuromorphic Hardware Accelerated Embedded Systems Using Dominant Sets for k-NN Prototype Selection Characterizing Humans on Riemannian Manifolds The Range of Pedestrian Detection with Automotive Radar Filtered channel features for pedestrian detection Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd Scale-Aware Fast R-CNN for Pedestrian Detection Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation Subcategory-Aware Convolutional Neural Networks for Object Proposals and Detection Taking a deeper look at pedestrians Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification A Survey of Deep Learning Applications to Autonomous Vehicle Control Combined generation of road marking and road sign databases applied to consistency checking of pedestrian crossings Roust Vanishing Point Detection Based on the Combination of Edge and Optical Flow EMS-Vision: combining on- and off-road driving Design a DC-DC Converter for a High Performance Electric Vehicle Performance comparison of a platooning scenorio using the IEEE 802.11p ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces Detection of unbalanced blade on UAV by means of audio signal
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ICACCI Intelligent Vehicles Symposium ICRA URAI ISoLA (1) INFOCOM Workshops 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICDM Workshops ICPR ECCV (14) SSRR IGSC ICIAP (2) 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV IEEE Transactions on Multimedia ArXiv 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) MVA ACIRS ICCVE 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ArXiv
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