Deep Learning Approach to Detection of Preceding Vehicle in Advanced Driver Assistance

In paper we propose a detection method for objects in video stream taken in front of a car by means of deep learning. The successful detection of preceding cars is a part of the analysis of current road situation including emergency and sudden braking, unintentional lane change, traffic jam, accident, etc. We include the results of preliminary experiments employing video stream captured by camera installed behind frontal wind screen. The detection and classification are performed using Convolutional Neural Network preceded by road lane detection. We performed several experiments on real-world data in order to check the accuracy of the proposed algorithm.

[1]  Przemyslaw Mazurek,et al.  Application of Shape Analysis Techniques for the Classification of Vehicles , 2010, TST.

[2]  Pawel Forczmanski,et al.  Surveillance Video Stream Analysis Using Adaptive Background Model and Object Recognition , 2010, ICCVG.

[3]  Pawel Forczmanski,et al.  Selected Aspects of Traffic Signs Recognition: Visual versus RFID Approach , 2013, TST.

[4]  Yong Tang,et al.  Vehicle detection and recognition for intelligent traffic surveillance system , 2017, Multimedia Tools and Applications.

[5]  Andrea Vedaldi,et al.  MatConvNet: Convolutional Neural Networks for MATLAB , 2014, ACM Multimedia.

[6]  Hong Zhao,et al.  A Rear-Vehicle Detection System for Static Images Based on Monocular Vision , 2006, 2006 9th International Conference on Control, Automation, Robotics and Vision.

[7]  Chung-Lin Huang,et al.  Real-time vision-based preceding vehicle tracking and recognition , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[8]  Pawel Forczmanski,et al.  Detecting Parked Vehicles in Static Images Using Simple Spectral Features in the 'SM4Public' System , 2015, ICIAR.

[9]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[10]  Yingfeng Cai,et al.  A Vehicle Detection Algorithm Based on Deep Belief Network , 2014, TheScientificWorldJournal.

[11]  Krzysztof Okarma,et al.  Fast Machine Vision Line Detection for Mobile Robot Navigation in Dark Environments , 2015, IP&C.

[12]  Junzo Watada,et al.  Detecting pedestrians and vehicles in traffic scene based on boosted HOG features and SVM , 2015, 2015 IEEE 9th International Symposium on Intelligent Signal Processing (WISP) Proceedings.

[13]  A. Jefferson Offutt,et al.  An Empirical Evaluation , 1994 .

[14]  Michal Wozniak,et al.  Pixel-Based Object Detection and Tracking with Ensemble of Support Vector Machines and Extended Structural Tensor , 2012, ICCCI.

[15]  S. Padmavathi,et al.  Vehicle Detection in Static Images Using Color and Corner Map , 2010, 2010 International Conference on Recent Trends in Information, Telecommunication and Computing.

[16]  Przemyslaw Mazurek,et al.  Application of Bayesian a Priori Distributions for Vehicles' Video Tracking Systems , 2010, TST.

[17]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[18]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[19]  Rama Chellappa,et al.  Higher order statistical learning for vehicle detection in images , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.