In addition to collecting raw data from several stoplights , the system was tested by creating and assessing several different types of forgery attack to see if they cause the identification algorithm to suffer a drop in accuracy . Ultimately , the purpose of this research is to create a more intelligent system in autonomous vehicles that will be able to detect forgery attacks on its camera system . This intelligent system would work to prevent situations where attacks on the system could result in collisions between vehicles and nearby pedestrians . Detecting objects in a two-dimensional setting is often insufficient in the context of real-life applications where the surrounding environment needs to be accurately recognized and oriented in three-dimension ( 3D ) , such as in the case of autonomous driving vehicles . Therefore , accurately and efficiently detecting objects in the three-dimensional setting is becoming increasingly relevant to a wide range of industrial applications , and thus is progressively attracting the attention of researchers . Combined databases of road markings and traffic signs provide a complete and full description of the present traffic legislation and instructions . Such databases contribute to efficient signage maintenance , improve navigation , and benefit autonomous driving vehicles . A system is presented for the automated creation of such combined databases , which additionally investigates the benefit of this combination for automated contextual placement analysis . The scope is limited to only solving problems which exist as a result of separating safety constraints and functionality . A literature review was done in order to identify common software architectural patterns used in connection with safety in autonomous road vehicles . The patterns were used to design a general architecture which was evaluated through application in an industrial case . Most research in the field of autonomous road vehicles is focused on lane tracking and other marker- dependent techniques . In these lines , a novel , computationally efficient method for ( front-view ) road identification and tracking based on monocular vision is proposed which is designed to work on roads independent of markings . Various ¿ Urban Transportation Systems ¿ are currently in developing , in order to put forward solutions to congestion and pollution in dense areas . Autonomous electric vehicles in free-access can be seen as an attractive approach , in view of the large flexibility that can be expected . One instrumental functionality linked to this solution is platoon motion : several autonomous vehicles accurately follow a trajectory defined on a dedicated circulation lane , with pre-specified inter-distances . Various # x201C;Urban Transportation Systems#x201D ; are currently in developing , in order to put forward solutions to congestion and pollution in dense areas . Autonomous electric vehicles in free-access can be seen as an attractive approach , in view of the large flexibility that can be expected . One instrumental functionality linked to this solution is platoon motion : several autonomous vehicles accurately follow the trajectory of a manually driven first vehicle , with pre-specified inter-distances . section : Introduction Pedestrian detection is an important research topic in computer vision field with various applications , such as autonomous driving , video surveillance , and robotics , which aims to predict a series of bounding boxes enclosing pedestrian instances in an image . Recent advances in object detection are driven by the success of deep convolutional neural networks ( CNNs ) , which uses the bounding box regression techniques to accurately localize the objects based on the deep features . 3D imaging technologies are applied in numerous areas , including self-driving cars , drones , and robots , and in advanced industrial , medical , scientific , and consumer applications . 3D imaging is usually accomplished by finding the distance to multiple points on an object or in a scene , and then creating a point cloud of those range measurements . In this paper , we describe the camera calibration and subsequent processing pipeline for multi-fisheye-camera systems developed as part of the V-Charge project . This project seeks to enable automated valet parking for self-driving cars . Our pipeline is able to precisely calibrate multi-camera systems , build sparse 3D maps for visual navigation , visually localize the car with respect to these maps , generate accurate dense maps , as well as detect obstacles based on real-time depth map extraction . Through the reflection of the plane mirror for the picture in a display screen , a driver can directly observe the rear view picture of the rear view dead angle area of the car tail part which is consistent to the left and the right positions of a common car backing mirror . The utility model is mainly used for the driver to completely observe the car tail part situation in backing and parking the car to enhance the safety for driving cars and prevent traffic accidents . The machines that are to be used in the field of robotics , medicines , industry needs to be so smart that they should be able to perform the day to day tasks easily without needing the human help . Take the case of self-driven cars , it is very crucial for the AI to make out the scenario in the real time and act accordingly take the necessary actions like breaking or evading in the traffics . Since the introduction of the object detection by the AI Researchers at Facebook , it has become easier for us to identify the objects in the images . Contrast , and colour-fidelity of images degraded by heavy fog should be restored to aid the ever emerging fields of computer vision applications . The revolutionary advent of self-driven cars has made it increasingly urgent that efficient and fast enhancement techniques be developed . Accumulation of dense fog is also a major contributor to road accidents . This paper introduces a millimeter-wave multiple-input-multiple-output ( MIMO ) antenna for autonomous ( self-driving ) cars . The antenna is a modified four-port balanced antipodal Vivaldi which produces four directional beams and provides pattern diversity to cover 90 deg angle of view . Autonomous and self-driving cars represent a certitude for the near future and it is expected to be safer , time saving and to bring in environmental benefits . The road infrastrucuture still embeds traffic signs that have to be recognized with high accuracy by the sensorial system mounted on an autonomous car . Multimedia has played an indispensable role in the success of various real-world applications , from smart healthcare to intelligent surveillance systems . In this new era of technology , one of these essential and useful applications is autonomous driving or self-driving cars . The idea of driverless vehicles was introduced many years ago when autopilot systems were designed for airplanes . Self-driving and autonomous cars are hot emerging technologies which can provide enormous impact in the near future . Since an important component of autonomous cars is vision processing , the increasing interest for self-driving cars has motivated researchers to collect different relative image datasets . By contrasting IoT ( Internet of Things ) with IT ( Information Technology ) , various evidences of the difference between them are discovered by our empirical and case studies . For empirical evidence , a comparison is made between the personal computer as representing a case of IT and the automobiles as representing a case of the future IoT , since the self-driving of cars is discussed quite frequently nowadays . Based on their patenting behavior , the degree of modularization is measured , and effects of digitalization on modularization are found out to differ between PC and automobile industries . High accurate vehicle positioning is essential in Vehicular Ad-Hoc Networks ( VANETs ) , and more specifically , for the advent of autonomously-driven vehicles . The global positioning system ( GPS ) does not provide accurate-enough and therefore suitable positioning services for applications like selfdriving vehicles . In this paper , we propose an enhanced scheme that makes use of the roadside units ( RSUs ) deployed in VANETs to get position estimates , and then improves their accuracy using cooperation among neighboring vehicles based on the received signal strength indicator ranging technique . Ape - X is designed for regimes in which it is possible to generate large quantities of data in parallel . This includes simulated environments but also a variety of real - world applications , such as robotic arm farms , self - driving cars , online recommender systems , or other multi - user systems in which data is generated by many instances of the same environment [ c.f. ][] concurrent - rl . In applications where data is costly to obtain , our approach will not be directly applicable . section : Introduction We focus on 3D object detection , which is a fundamental computer vision problem impacting most autonomous robotics systems including self - driving cars and drones . The goal of 3D object detection is to recover the 6 DoF pose and the 3D bounding box dimensions for all objects of interest in the scene . Many existing algorithms also make strong domain - specific assumptions . For example , MV3D assumes that all objects can be segmented in a top - down 2D view of the point cloud , which works for the common self - driving case but does not generalize to indoor scenes where objects can be placed on top of each other . Furthermore , the top - down view approach tends to work well for objects such as cars , but does not for other key object classes such as pedestrians or cyclists . Traffic-sign recognition ( TSR ) is an essential component of a driver assistance system ( DAS ) , providing drivers with safety and precaution information . In this paper , we evaluate the performance of k-d trees , random forests , and support vector machines ( SVMs ) for traffic-sign classification using different-sized histogram-of-oriented-gradient ( HOG ) descriptors and distance transforms ( DTs ) . Of late , traffic sign detection and recognition are becoming very prevalent topic as it enhances drivers towards safety and alert them with precaution information . This study reports about processing time of the individual color detection and recognition of the partial occlusion traffic sign that have been previously implemented using HSV and RGB color ratio and ANN and PCA method respectively for detection and recognition . Traffic sign recognition is an important method that improves the safety in the roads , and this system is an additional step to autonomous driving . Nowadays , to solve traffic sign recognition problem , convolutional neural networks ( CNN ) can be adopted for its high performance well proved for computer vision applications . This paper proposes histogram equalization preprocessing ( HOG ) and CNN with additional operations – batch normalization , dropout and data augmentation . Identifying rich features using modern machine learning methods has recently attracted attention and has achieved success in many benchmarks . However these approaches have not been fully implemented in the traffic sign recognition problem . In this paper , we propose a new approach to tackle the traffic sign recognition problem . The proposed method enables us to obtain training data of all categories without exhaustively collecting them . Experimental results show the effectiveness of the proposed method for traffic sign symbol recognition . A Quantitative Study . Deep learning in traffic sign detection & recognition ( TSDR ) is widely explored in recent times due to its ability to produce state-of-the-art results and availability of public datasets . Two different architectures of detection networks are currently being developed : Single Shot and Region Proposal based approaches . * ShortestPath.zip * Policing function for traffic sign image recognition - * TrafficSigns.zip * Policing function for finding shortest paths program Using the Tsinghua dataset with a synthetic counterpart , SYNTHIA-TS , we have run an extensive set of experiments . The results show that the proposed method is indeed effective , provided that we use a proper Convolutional Neural Network ( CNN ) to perform the traffic sign recognition ( classification ) task as well as a proper GAN to transform the synthetic images . Here , a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio ∼1/4 for new/known classes ; even for more challenging ratios such as ∼4/1 , the results are also very positive . There are many applications for image processing in ITS . Typical on-board applications are : lane marker detection , obstacle detection , proximity vehicle detection , traffic signal/sign recognition , road surface monitoring , driver monitoring , etc . Roadside applications are traffic surveillance ( traffic flow , travel time ) , incident/accident detection , license number recognition ( ETC , parking lot ) and parking lot surveillance . document : Multi - column Deep Neural Networks for Image Classification Traditional methods of computer vision and machine learning can not match human performance on tasks such as the recognition of handwritten digits or traffic signs . Our biologically plausible deep artificial neural network architectures can . On the very competitive MNIST handwriting benchmark , our method is the first to achieve near - human performance . On a traffic sign recognition benchmark it outperforms humans by a factor of two . We also improve the state - of - the - art on a plethora of common image classification benchmarks . The multi - scale features described in Section [ reference ] aim at exploiting more context at very low computational cost . They are inspired by the recent success of combining information at multiple scales in tasks like traffic sign recognition , semantic segmentation and depth map prediction . In LVCSR the multi - scale idea has been explored in tandem systems and the CLDNN architecture . Our method does not need any special implementations . We evaluate our method on the digit classification task , traffic sign classification task and sentiment analysis task using the Amazon Review dataset , and demonstrate state - of - the - art performance in nearly all experiments . In particular , in the adaptation scenario , MNIST→SVHN , our method outperformed other methods by more than 10 % . However , due to large vehicle scale variation , heavy occlusion , or truncation of the vehicle in an image , recent deep CNN-based object detectors still showed a limited performance . This paper proposes an improved framework based on Faster R-CNN for fast vehicle detection . Firstly , MobileNet architecture is adopted to build the base convolution layer in Faster R-CNN . We perform fast vehicle detection from traffic surveillance cameras . A novel deep learning framework , namely Evolving Boxes , is developed that proposes and refines the object boxes under different feature representations . In this paper , we delve into the source of scale sensitivity , and reveal two key issues : 1 ) existing RoI pooling destroys the structure of small scale objects and 2 ) the large intra-class distance for a large variance of scales exceeds the representation capability of a single network . Based on these findings , we present a scale-insensitive convolutional neural network ( SINet ) for fast detecting vehicles with a large variance of scales . First , we present a context-aware RoI pooling to maintain the contextual information and original structure of small scale objects . However , the existing CNN-based feature extraction algorithms , especially residual network , can not obtain powerful semantic information in the vehicle detection task , and thus suffer from the problem of a missing detection , error detection , or repeated detection . In this paper , we present a connect-and-merge convolutional neural network ( CMNet ) for fast detecting vehicles in complex scenes . First , we propose a connect-and-merge residual network ( CMRN ) for performing feature extraction . The detection of fast moving vehicle is an important part in Intelligent Transportation System . This paper presents a new method for detecting vehicles , which violate rules in real time traffic scenario . Fast and accurate vehicle detection in unmanned aerial vehicle ( UAV ) imagery is a meaningful but challenging task , playing an important role in a wide range of applications . Due to its tiny size , few features , variable scales and imbalance vehicle sample problems in UAV imagery , current deep learning methods used in this task can not achieve a satisfactory performance both in accuracy and speed , which is obvious a classical trade-off problem . Fast detection of moving vehicle is essential for autonomous driving . In this paper we propose fast vehicle classification algorithm for autonomous vehicle using orientation histogram and segmented line projection , and also works well with in urban environment . In this paper , a new two-stage regression based cascade object detection system is proposed . This system can be fast detection of the vehicles which concentrated the advantages of the two aforementioned methods , denoted by HybridNet . In our design , the first and the second stage are both regression modes . Finally , the tracking of vehicles assists to stabilize the detection results . The experiments show that the average processing speed reaches 39 frames/ms , and the fast detection of vehicle under different weather conditions in the day time can also work accurately . Unfortunately , due to a great diversity of vehicle profiles and outdoor illumination conditions , it is a challenge to detect vehicles effectively . This paper proposes a method for high-performance vehicle detection based on visual attention mechanism and AdaBoost cascade classifier . Our method constructs the structural Haar features and extracts the features of samples using structural Haar features and trains an AdaBoost cascade classifier . The algorithm is designed to be part of a vehicle tracking system . The main aim is to integrate information from Gabor and Mallat ( 1992 ) wavelet transforms to improve the accuracy and speed of vehicle detection . The Gabor-wavelet analysis allows a rapid estimation of image flow vectors with low spatial resolution . We describe the model and similarities in the next section . section : Real Time Vehicle Detection Convolutional Neural Networks ( CNNs ) have had the largest success in image recognition in the past 3 years .
In addition to collecting raw data from several stoplights , the system was tested by creating and assessing several different types of forgery attack to see if they cause the identification algorithm to suffer a drop in accuracy . Ultimately , the purpose of this research is to create a more intelligent system in autonomous vehicles that will be able to detect forgery attacks on its camera system . This intelligent system would work to prevent situations where attacks on the system could result in collisions between vehicles and nearby pedestrians .
Detecting objects in a two-dimensional setting is often insufficient in the context of real-life applications where the surrounding environment needs to be accurately recognized and oriented in three-dimension ( 3D ) , such as in the case of autonomous driving vehicles . Therefore , accurately and efficiently detecting objects in the three-dimensional setting is becoming increasingly relevant to a wide range of industrial applications , and thus is progressively attracting the attention of researchers .
Combined databases of road markings and traffic signs provide a complete and full description of the present traffic legislation and instructions . Such databases contribute to efficient signage maintenance , improve navigation , and benefit autonomous driving vehicles . A system is presented for the automated creation of such combined databases , which additionally investigates the benefit of this combination for automated contextual placement analysis .
The scope is limited to only solving problems which exist as a result of separating safety constraints and functionality . A literature review was done in order to identify common software architectural patterns used in connection with safety in autonomous road vehicles . The patterns were used to design a general architecture which was evaluated through application in an industrial case .
Most research in the field of autonomous road vehicles is focused on lane tracking and other marker- dependent techniques . In these lines , a novel , computationally efficient method for ( front-view ) road identification and tracking based on monocular vision is proposed which is designed to work on roads independent of markings .
Various ¿ Urban Transportation Systems ¿ are currently in developing , in order to put forward solutions to congestion and pollution in dense areas . Autonomous electric vehicles in free-access can be seen as an attractive approach , in view of the large flexibility that can be expected . One instrumental functionality linked to this solution is platoon motion : several autonomous vehicles accurately follow a trajectory defined on a dedicated circulation lane , with pre-specified inter-distances .
Various # x201C;Urban Transportation Systems#x201D ; are currently in developing , in order to put forward solutions to congestion and pollution in dense areas . Autonomous electric vehicles in free-access can be seen as an attractive approach , in view of the large flexibility that can be expected . One instrumental functionality linked to this solution is platoon motion : several autonomous vehicles accurately follow the trajectory of a manually driven first vehicle , with pre-specified inter-distances .
section : Introduction Pedestrian detection is an important research topic in computer vision field with various applications , such as autonomous driving , video surveillance , and robotics , which aims to predict a series of bounding boxes enclosing pedestrian instances in an image . Recent advances in object detection are driven by the success of deep convolutional neural networks ( CNNs ) , which uses the bounding box regression techniques to accurately localize the objects based on the deep features .
3D imaging technologies are applied in numerous areas , including self-driving cars , drones , and robots , and in advanced industrial , medical , scientific , and consumer applications . 3D imaging is usually accomplished by finding the distance to multiple points on an object or in a scene , and then creating a point cloud of those range measurements .
In this paper , we describe the camera calibration and subsequent processing pipeline for multi-fisheye-camera systems developed as part of the V-Charge project . This project seeks to enable automated valet parking for self-driving cars . Our pipeline is able to precisely calibrate multi-camera systems , build sparse 3D maps for visual navigation , visually localize the car with respect to these maps , generate accurate dense maps , as well as detect obstacles based on real-time depth map extraction .
Through the reflection of the plane mirror for the picture in a display screen , a driver can directly observe the rear view picture of the rear view dead angle area of the car tail part which is consistent to the left and the right positions of a common car backing mirror . The utility model is mainly used for the driver to completely observe the car tail part situation in backing and parking the car to enhance the safety for driving cars and prevent traffic accidents .
The machines that are to be used in the field of robotics , medicines , industry needs to be so smart that they should be able to perform the day to day tasks easily without needing the human help . Take the case of self-driven cars , it is very crucial for the AI to make out the scenario in the real time and act accordingly take the necessary actions like breaking or evading in the traffics . Since the introduction of the object detection by the AI Researchers at Facebook , it has become easier for us to identify the objects in the images .
Contrast , and colour-fidelity of images degraded by heavy fog should be restored to aid the ever emerging fields of computer vision applications . The revolutionary advent of self-driven cars has made it increasingly urgent that efficient and fast enhancement techniques be developed . Accumulation of dense fog is also a major contributor to road accidents .
This paper introduces a millimeter-wave multiple-input-multiple-output ( MIMO ) antenna for autonomous ( self-driving ) cars . The antenna is a modified four-port balanced antipodal Vivaldi which produces four directional beams and provides pattern diversity to cover 90 deg angle of view .
Autonomous and self-driving cars represent a certitude for the near future and it is expected to be safer , time saving and to bring in environmental benefits . The road infrastrucuture still embeds traffic signs that have to be recognized with high accuracy by the sensorial system mounted on an autonomous car .
Multimedia has played an indispensable role in the success of various real-world applications , from smart healthcare to intelligent surveillance systems . In this new era of technology , one of these essential and useful applications is autonomous driving or self-driving cars . The idea of driverless vehicles was introduced many years ago when autopilot systems were designed for airplanes .
Self-driving and autonomous cars are hot emerging technologies which can provide enormous impact in the near future . Since an important component of autonomous cars is vision processing , the increasing interest for self-driving cars has motivated researchers to collect different relative image datasets .
By contrasting IoT ( Internet of Things ) with IT ( Information Technology ) , various evidences of the difference between them are discovered by our empirical and case studies . For empirical evidence , a comparison is made between the personal computer as representing a case of IT and the automobiles as representing a case of the future IoT , since the self-driving of cars is discussed quite frequently nowadays . Based on their patenting behavior , the degree of modularization is measured , and effects of digitalization on modularization are found out to differ between PC and automobile industries .
High accurate vehicle positioning is essential in Vehicular Ad-Hoc Networks ( VANETs ) , and more specifically , for the advent of autonomously-driven vehicles . The global positioning system ( GPS ) does not provide accurate-enough and therefore suitable positioning services for applications like selfdriving vehicles . In this paper , we propose an enhanced scheme that makes use of the roadside units ( RSUs ) deployed in VANETs to get position estimates , and then improves their accuracy using cooperation among neighboring vehicles based on the received signal strength indicator ranging technique .
Ape - X is designed for regimes in which it is possible to generate large quantities of data in parallel . This includes simulated environments but also a variety of real - world applications , such as robotic arm farms , self - driving cars , online recommender systems , or other multi - user systems in which data is generated by many instances of the same environment [ c.f. ][] concurrent - rl . In applications where data is costly to obtain , our approach will not be directly applicable .
section : Introduction We focus on 3D object detection , which is a fundamental computer vision problem impacting most autonomous robotics systems including self - driving cars and drones . The goal of 3D object detection is to recover the 6 DoF pose and the 3D bounding box dimensions for all objects of interest in the scene .
Many existing algorithms also make strong domain - specific assumptions . For example , MV3D assumes that all objects can be segmented in a top - down 2D view of the point cloud , which works for the common self - driving case but does not generalize to indoor scenes where objects can be placed on top of each other . Furthermore , the top - down view approach tends to work well for objects such as cars , but does not for other key object classes such as pedestrians or cyclists .
Traffic-sign recognition ( TSR ) is an essential component of a driver assistance system ( DAS ) , providing drivers with safety and precaution information . In this paper , we evaluate the performance of k-d trees , random forests , and support vector machines ( SVMs ) for traffic-sign classification using different-sized histogram-of-oriented-gradient ( HOG ) descriptors and distance transforms ( DTs ) .
Of late , traffic sign detection and recognition are becoming very prevalent topic as it enhances drivers towards safety and alert them with precaution information . This study reports about processing time of the individual color detection and recognition of the partial occlusion traffic sign that have been previously implemented using HSV and RGB color ratio and ANN and PCA method respectively for detection and recognition .
Traffic sign recognition is an important method that improves the safety in the roads , and this system is an additional step to autonomous driving . Nowadays , to solve traffic sign recognition problem , convolutional neural networks ( CNN ) can be adopted for its high performance well proved for computer vision applications . This paper proposes histogram equalization preprocessing ( HOG ) and CNN with additional operations – batch normalization , dropout and data augmentation .
Identifying rich features using modern machine learning methods has recently attracted attention and has achieved success in many benchmarks . However these approaches have not been fully implemented in the traffic sign recognition problem . In this paper , we propose a new approach to tackle the traffic sign recognition problem .
The proposed method enables us to obtain training data of all categories without exhaustively collecting them . Experimental results show the effectiveness of the proposed method for traffic sign symbol recognition .
A Quantitative Study . Deep learning in traffic sign detection & recognition ( TSDR ) is widely explored in recent times due to its ability to produce state-of-the-art results and availability of public datasets . Two different architectures of detection networks are currently being developed : Single Shot and Region Proposal based approaches .
* ShortestPath.zip * Policing function for traffic sign image recognition - * TrafficSigns.zip * Policing function for finding shortest paths program
Using the Tsinghua dataset with a synthetic counterpart , SYNTHIA-TS , we have run an extensive set of experiments . The results show that the proposed method is indeed effective , provided that we use a proper Convolutional Neural Network ( CNN ) to perform the traffic sign recognition ( classification ) task as well as a proper GAN to transform the synthetic images . Here , a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio ∼1/4 for new/known classes ; even for more challenging ratios such as ∼4/1 , the results are also very positive .
There are many applications for image processing in ITS . Typical on-board applications are : lane marker detection , obstacle detection , proximity vehicle detection , traffic signal/sign recognition , road surface monitoring , driver monitoring , etc . Roadside applications are traffic surveillance ( traffic flow , travel time ) , incident/accident detection , license number recognition ( ETC , parking lot ) and parking lot surveillance .
document : Multi - column Deep Neural Networks for Image Classification Traditional methods of computer vision and machine learning can not match human performance on tasks such as the recognition of handwritten digits or traffic signs . Our biologically plausible deep artificial neural network architectures can .
On the very competitive MNIST handwriting benchmark , our method is the first to achieve near - human performance . On a traffic sign recognition benchmark it outperforms humans by a factor of two . We also improve the state - of - the - art on a plethora of common image classification benchmarks .
The multi - scale features described in Section [ reference ] aim at exploiting more context at very low computational cost . They are inspired by the recent success of combining information at multiple scales in tasks like traffic sign recognition , semantic segmentation and depth map prediction . In LVCSR the multi - scale idea has been explored in tandem systems and the CLDNN architecture .
Our method does not need any special implementations . We evaluate our method on the digit classification task , traffic sign classification task and sentiment analysis task using the Amazon Review dataset , and demonstrate state - of - the - art performance in nearly all experiments . In particular , in the adaptation scenario , MNIST→SVHN , our method outperformed other methods by more than 10 % .
However , due to large vehicle scale variation , heavy occlusion , or truncation of the vehicle in an image , recent deep CNN-based object detectors still showed a limited performance . This paper proposes an improved framework based on Faster R-CNN for fast vehicle detection . Firstly , MobileNet architecture is adopted to build the base convolution layer in Faster R-CNN .
We perform fast vehicle detection from traffic surveillance cameras . A novel deep learning framework , namely Evolving Boxes , is developed that proposes and refines the object boxes under different feature representations .
In this paper , we delve into the source of scale sensitivity , and reveal two key issues : 1 ) existing RoI pooling destroys the structure of small scale objects and 2 ) the large intra-class distance for a large variance of scales exceeds the representation capability of a single network . Based on these findings , we present a scale-insensitive convolutional neural network ( SINet ) for fast detecting vehicles with a large variance of scales . First , we present a context-aware RoI pooling to maintain the contextual information and original structure of small scale objects .
However , the existing CNN-based feature extraction algorithms , especially residual network , can not obtain powerful semantic information in the vehicle detection task , and thus suffer from the problem of a missing detection , error detection , or repeated detection . In this paper , we present a connect-and-merge convolutional neural network ( CMNet ) for fast detecting vehicles in complex scenes . First , we propose a connect-and-merge residual network ( CMRN ) for performing feature extraction .
The detection of fast moving vehicle is an important part in Intelligent Transportation System . This paper presents a new method for detecting vehicles , which violate rules in real time traffic scenario .
Fast and accurate vehicle detection in unmanned aerial vehicle ( UAV ) imagery is a meaningful but challenging task , playing an important role in a wide range of applications . Due to its tiny size , few features , variable scales and imbalance vehicle sample problems in UAV imagery , current deep learning methods used in this task can not achieve a satisfactory performance both in accuracy and speed , which is obvious a classical trade-off problem .
Fast detection of moving vehicle is essential for autonomous driving . In this paper we propose fast vehicle classification algorithm for autonomous vehicle using orientation histogram and segmented line projection , and also works well with in urban environment .
In this paper , a new two-stage regression based cascade object detection system is proposed . This system can be fast detection of the vehicles which concentrated the advantages of the two aforementioned methods , denoted by HybridNet . In our design , the first and the second stage are both regression modes .
Finally , the tracking of vehicles assists to stabilize the detection results . The experiments show that the average processing speed reaches 39 frames/ms , and the fast detection of vehicle under different weather conditions in the day time can also work accurately .
Unfortunately , due to a great diversity of vehicle profiles and outdoor illumination conditions , it is a challenge to detect vehicles effectively . This paper proposes a method for high-performance vehicle detection based on visual attention mechanism and AdaBoost cascade classifier . Our method constructs the structural Haar features and extracts the features of samples using structural Haar features and trains an AdaBoost cascade classifier .
The algorithm is designed to be part of a vehicle tracking system . The main aim is to integrate information from Gabor and Mallat ( 1992 ) wavelet transforms to improve the accuracy and speed of vehicle detection . The Gabor-wavelet analysis allows a rapid estimation of image flow vectors with low spatial resolution .
We describe the model and similarities in the next section . section : Real Time Vehicle Detection Convolutional Neural Networks ( CNNs ) have had the largest success in image recognition in the past 3 years .
Stoplight Detection: Implementation and Forgery Attacks Class-specific Anchoring Proposal for 3D Object Recognition in LIDAR and RGB Images Exploiting automatically generated databases of traffic signs and road markings for contextual co-occurrence analysis Software Architecture for Reducing Testing Effort while Ensuring Safety in Autonomous Road Vehicles High Speed Autonomous Navigation of Unmanned Aerial Vehicles using novel Road Identification, Following & Tracking (RIFT) Algorithm* Obstacle avoidance and trajectory replanification for a group of communicating vehicles On-line reference trajectory generation for manually convoying a platoon of automatic urban vehicles Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd Lidar System Architectures and Circuits 3D visual perception for self-driving cars using a multi-camera system: Calibration, mapping, localization, and obstacle detection Car with folding electronic rear-view mirror system An Optimised Approach on Object and Text Detection from Real Time Data using Histogram Equalization Technique Single Image Visibility Restoration Using Dark Channel Prior and Fuzzy Logic Millimeter-Wave MIMO Balanced Antipodal Vivaldi Antenna Design for Autonomous Cars Neural Network Based Traffic Sign Recognition for Autonomous Driving Multimedia for Autonomous Driving Image dataset for Persian Road Surface Markings An Empirical Study of Contrasting IoT with IT: Evidences of Differences Drawn from Japanese Experiences A cooperative and roadside unit-aided localization scheme for vehicular ad hoc networks Distributed Prioritized Experience Replay PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation Real-Time Traffic-Sign Recognition Using Tree Classifiers Individual processing speed analysis for traffic sign detection and recognition Traffic sign recognition using convolutional neural networks / Kelio ženklų atpažinimas naudojant neuroninį tinklą Traffic sign recognition using group sparse coding Generation of Training Data by Degradation Models for Traffic Sign Symbol Recognition Sign Recognition - How well does Single Shot Multibox Detector sum up? A Quantitative Study Rodin Developments for Formal Policing Functions based on Metamorphic Relations Recognizing New Classes with Synthetic Data in the Loop: Application to Traffic Sign Recognition Imaging and analysis of traffic scene Multi-column deep neural networks for image classification Very deep multilingual convolutional neural networks for LVCSR Asymmetric Tri-training for Unsupervised Domain Adaptation Improving Faster R-CNN Framework for Fast Vehicle Detection Evolving boxes for fast vehicle detection SINet: A Scale-Insensitive Convolutional Neural Network for Fast Vehicle Detection CMNet: A Connect-and-Merge Convolutional Neural Network for Fast Vehicle Detection in Urban Traffic Surveillance Fast Moving Vehicle Number Plate Detection Real-Time Vehicle Detection from UAV Imagery Fast vehicle detection using orientation histogram and segmented line projection HybridNet: A fast vehicle detection system for autonomous driving Real-time on-road vehicle detection algorithm based on monocular vision Vehicle detection based on visual attention mechanism and adaboost cascade classifier in intelligent transportation systems Wavelet transforms for use in motion detection and tracking application An Empirical Evaluation of Deep Learning on Highway Driving
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IROS ECCV ISNCC ICLR 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition AIPR ICIP (4) 2012 IEEE Conference on Computer Vision and Pattern Recognition 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ICML ICME BigMM ArXiv
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