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Automatic Detection and Classification of Road, Car, and Pedestrian Using Binocular Cameras in Traffic Scenes with a Common Framework

Yongchao Song, Jieru Yao, Yongfeng Ju, Yahong Jiang, Kai Du
2020 Complexity  
Vehicle detection results on the road are confirmed by combining disparity and color energy minimum algorithms with the object window aspect ratio threshold method.  ...  In order to solve the problems of traffic object detection, fuzzification, and simplification in real traffic environment, an automatic detection and classification algorithm for roads, vehicles, and pedestrians  ...  In recent years, with the rapid development of deep learning technology, many methods of applying deep learning have appeared to realize the detection of traffic objects. Han et al.  ... 
doi:10.1155/2020/2435793 doaj:19fe19747e83438da14bc6b012f352eb fatcat:d2gmnjuwxnfx5ix6w4ci6yb62i

Object Detection and Depth Estimation Approach Based on Deep Convolutional Neural Networks

Huai-Mu Wang, Huei-Yung Lin, Chin-Chen Chang
2021 Sensors  
In this paper, we present a real-time object detection and depth estimation approach based on deep convolutional neural networks (CNNs).  ...  Finally, we integrate the two-dimensional (2D) location of the detected object with the depth information to achieve real-time detection and depth estimation.  ...  Conclusions We presented an object detection and depth estimation approach based on deep learning techniques.  ... 
doi:10.3390/s21144755 fatcat:vktbvkf2qbclrlle6beauwdwfu

Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art [article]

Joel Janai, Fatma Güney, Aseem Behl, Andreas Geiger
2021 arXiv   pre-print
Recent years have witnessed enormous progress in AI-related fields such as computer vision, machine learning, and autonomous vehicles.  ...  To ease accessibility and accommodate missing references, we also provide a website that allows navigating topics as well as methods and provides additional information.  ...  Object detection and free-space analysis were performed using radar and stereo vision. Monocular vision was used for traffic light detection and object classification.  ... 
arXiv:1704.05519v3 fatcat:xiintiarqjbfldheeg2hsydyra

Real Time Vehicle Detection, Tracking, and Inter-vehicle Distance Estimation based on Stereovision and Deep Learning using YOLOv3

Omar BOURJA, Hatim DERROUZ, Hamd AIT ABDELALI, Abdelilah MAACH, Rachid OULAD HAJ THAMI, Francois BOURZEIX
2021 International Journal of Advanced Computer Science and Applications  
Traffic images are captured by a stereoscopic system installed on the road, and then we detect moving vehicles with the YOLO V3 Deep Neural Network algorithm.  ...  In this paper, we propose a robust real-time vehicle tracking and inter-vehicle distance estimation algorithm based on stereovision.  ...  Combinations of two deep learning models are developed to achieve object detection and tracking. The algorithms are tested on both railway and environment.  ... 
doi:10.14569/ijacsa.2021.01208101 fatcat:3ag46nxoajfcbjrq5psctckjnu

Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modeling [article]

Sebastian Ramos, Stefan Gehrig, Peter Pinggera, Uwe Franke, Carsten Rother
2016 arXiv   pre-print
To utilize the appearance and contextual cues, we propose a new deep learning-based obstacle detection framework.  ...  We present a principled Bayesian framework to fuse the semantic and stereo-based detection results.  ...  Acknowledgments: We would like to thank Nicolai Schneider for his support during testing and evaluation.  ... 
arXiv:1612.06573v1 fatcat:mctl7gt52jac3gfaalxhf3t6bu

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Qi Liu, Zirui Li, Shihua Yuan, Yuzheng Zhu, Xueyuan Li
2021 Sensors  
Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance.  ...  Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms.  ...  In addition to deep learning methods, in [218] stereo vision and 2D Lidar were integrated for vehicle detection.  ... 
doi:10.3390/s21041354 pmid:33672976 fatcat:ammlsccxbbhgpkx6r5vod7ciuy

DETECTION AND LOCALIZATION OF TRAFFIC LIGHTS USING YOLOV3 AND STEREO VISION

W. Omar, I. Lee, G. Lee, K. M. Park
2020 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
This paper proposes an integrated method for improving the detection accuracy and traffic lights colours classification while supporting a real-time operation by modelling the bounding box (bbox) of YOLOv3  ...  In this paper, an algorithm to detect, classify the traffic light colours and spatially locate traffic light are integrated.  ...  A deep learning approach to traffic lights: Detection, tracking, and classification. IEEE International Conference on Robotics and Automation (ICRA). D. Eigen, C. P. (2014).  ... 
doi:10.5194/isprs-archives-xliii-b2-2020-1247-2020 fatcat:lx25okxjtnfxbekyormi3q34ta

Salient Bundle Adjustment for Visual SLAM [article]

Ke Wang, Sai Ma, Junlan Chen, Jianbo Lu
2020 arXiv   pre-print
Therefore, we proposed a saliency model to predict the saliency map, which can capture both scene semantic and geometric information.  ...  Exhaustive experiments conducted with the state-of-the-art algorithm in KITTI and EuRoc datasets show that our proposed algorithm outperforms existing algorithms in both indoor and outdoor environments  ...  Besides, many researchers attempt to combine geometrybased method with deep learning-based method [24] , [25] , [64] .  ... 
arXiv:2012.11863v1 fatcat:e6mrc6nnfnhutbi6a4y7bn7qz4

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

Jianjun Ni, Yinan Chen, Yan Chen, Jinxiu Zhu, Deena Ali, Weidong Cao
2020 Applied Sciences  
Then the main problems in self-driving cars and their solutions based on deep learning methods are analyzed, such as obstacle detection, scene recognition, lane detection, navigation and path planning.  ...  This survey provides a detailed explanation of the developments of self-driving cars and summarizes the applications of deep learning methods in the field of self-driving cars.  ...  The solution of the stereo matching algorithm lays a solid foundation for obstacle determination. There are other methods based on deep learning used in the obstacle detection for self-driving cars.  ... 
doi:10.3390/app10082749 fatcat:iohm7uqj2vbojmnao6kyhzeliu

Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and $k$ -Nearest Neighbor Scheme

Abdelkader Dairi, Fouzi Harrou, Ying Sun, Mohamed Senouci
2018 IEEE Sensors Journal  
The proposed method uses a deep stacked auto-encoders (DSA) model that combines the greedy learning features with the dimensionality reduction capacity and employs an unsupervised k-nearest neighbors algorithm  ...  In this study, we propose a stereovisionbased method for detecting obstacles in urban environment.  ...  In this paper, we proposed a novel stereo vision method capable of detecting obstacles in a road environment.  ... 
doi:10.1109/jsen.2018.2831082 fatcat:vamazlpvjvflfdmfq7cczvr3se

Driving Datasets Literature Review [article]

Charles-Éric Noël Laflamme, François Pomerleau, Philippe Giguère
2019 arXiv   pre-print
This report is a survey of the different autonomous driving datasets which have been published up to date. The first section introduces the many sensor types used in autonomous driving datasets.  ...  The second section investigates the calibration and synchronization procedure required to generate accurate data. The third section describes the diverse driving tasks explored by the datasets.  ...  Acknowledgments This work was supported by NSERC CRD Grant "BRITE: Bus RapId Transit systEm" (511843) and Consortium InnovÉÉ. Page 31 of 39 Driving Datasets Literature Review  ... 
arXiv:1910.11968v1 fatcat:l6a7rbwfafdujmzy527sexmxle

Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies [article]

Yu Huang, Yue Chen
2020 arXiv   pre-print
This is a survey of autonomous driving technologies with deep learning methods.  ...  Almost at the same time, deep learning has made breakthrough by several pioneers, three of them (also called fathers of deep learning), Hinton, Bengio and LeCun, won ACM Turin Award in 2019.  ...  algorithms of optimization, computer vision and machine learning.  ... 
arXiv:2006.06091v3 fatcat:nhdgivmtrzcarp463xzqvnxlwq

Free-Space Detection with Self-Supervised and Online Trained Fully Convolutional Networks [article]

Willem P. Sanberg, Gijs Dubbelman, Peter H.N. de With
2017 arXiv   pre-print
We have validated our algorithm using publicly available data and on a new challenging benchmark dataset that is released with this paper.  ...  To this end, our self-supervised training relies on a stereo-vision disparity system, to automatically generate (weak) training labels for the color-based FCN.  ...  -A. Brust with employing the CN24 library.  ... 
arXiv:1604.02316v2 fatcat:yngqtwnv4fgrdhlo262entiqv4

3D Scene Understanding at Urban Intersection Using Stereo Vision and Digital Map

Prarthana Bhattacharyya, Yanlei Gu, Jiali Bao, Xu Liu, Shunsuke Kamijo
2017 2017 IEEE 85th Vehicular Technology Conference (VTC Spring)  
Stereo vision is used to detect, classify and track obstacles, while a 3D digital map is used to improve ego-localization and provide context in terms of road-layout information.  ...  In this paper, we introduce a stereo vision and 3D digital map based approach to spatially and temporally analyze the traffic situation at urban intersections.  ...  The semantic and geometric cues are obtained from stereo disparity generation algorithms and deep learning based methods.  ... 
doi:10.1109/vtcspring.2017.8108283 dblp:conf/vtc/BhattacharyyaGB17 fatcat:um42cudbqrhwzkowwgh6lp4ww4

Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras [article]

Ciaran Eising, Jonathan Horgan, Senthil Yogamani
2021 arXiv   pre-print
They provide high information density and are optimal for detecting road infrastructure cues laid out for human vision.  ...  They are the principal sensors for low-speed, high accuracy, and close-range sensing applications, such as automated parking, traffic jam assistance, and low-speed emergency braking.  ...  Many thanks to Edward Jones (NUI Galway) and Matthieu Cord (Sorbonne University and Valeo.ai) for providing a detailed review prior to submission.  ... 
arXiv:2103.17001v3 fatcat:uyy2ieomf5cajhened27n3n2mm
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