Tracking as a Whole: Multi-Target Tracking by Modeling Group Behavior With Sequential Detection

Video-based vehicle detection and tracking is one of the most important components for intelligent transportation systems. When it comes to road junctions, the problem becomes even more difficult due to the occlusions and complex interactions among vehicles. In order to get a precise detection and tracking result, in this paper we propose a novel tracking-by-detection framework. In the detection stage, we present a sequential detection model to deal with serious occlusions. In the tracking stage, we model group behavior to treat complex interactions with overlaps and ambiguities. The main contributions of this paper are twofold: 1) shape prior is exploited in the sequential detection model to tackle occlusions in crowded scene and 2) traffic force is defined in the traffic scene to model group behavior, and it can assist to handle complex interactions among vehicles. We evaluate the proposed approach on real surveillance videos at road junctions and the performance has demonstrated the effectiveness of our method.

[1]  A. Das,et al.  A Novel Algorithm for Night Time Vehicle Detection Even with One Non-functional Taillight by CIOF (Color Inherited Optical Flow) , 2016, ICPR 2016.

[2]  Mohamed R. Amer,et al.  Multiobject tracking as maximum weight independent set , 2011, CVPR 2011.

[3]  Gordon Morison,et al.  Selective Sampling Importance Resampling Particle Filter Tracking With Multibag Subspace Restoration , 2018, IEEE Transactions on Cybernetics.

[4]  Qi Wang,et al.  Multi-cue based tracking , 2014, Neurocomputing.

[5]  Bing Wang,et al.  Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Pietro Perona,et al.  Fast Feature Pyramids for Object Detection , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Chunhong Pan,et al.  Forward–Backward Mean-Shift for Visual Tracking With Local-Background-Weighted Histogram , 2013, IEEE Transactions on Intelligent Transportation Systems.

[8]  ZissermanAndrew,et al.  The Pascal Visual Object Classes Challenge , 2015 .

[9]  P. Cerri,et al.  Obstacle detection and classification fusing radar and vision , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[10]  Hongbin Zha,et al.  Probabilistic Inference for Occluded and Multiview On-road Vehicle Detection , 2016, IEEE Transactions on Intelligent Transportation Systems.

[11]  Rainer Stiefelhagen,et al.  Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics , 2008, EURASIP J. Image Video Process..

[12]  Xin Li,et al.  A real-time vehicle detection and tracking system in outdoor traffic scenes , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[13]  Shai Avidan,et al.  Ensemble Tracking , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Roberto Hirata,et al.  Vehicle Detection Using Mixture of Deformable Parts Models: Static and Dynamic Camera , 2012, 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images.

[16]  Jianguo Zhang,et al.  The PASCAL Visual Object Classes Challenge , 2006 .

[17]  Thomas Bräunl,et al.  Symmetry-based monocular vehicle detection system , 2011, Machine Vision and Applications.

[18]  Ramakant Nevatia,et al.  Global data association for multi-object tracking using network flows , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Qi Wang,et al.  Robust Superpixel Tracking via Depth Fusion , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[20]  C. Laurgeau,et al.  Vehicle detection combining gradient analysis and AdaBoost classification , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[21]  Mohan M. Trivedi,et al.  A General Active-Learning Framework for On-Road Vehicle Recognition and Tracking , 2010, IEEE Transactions on Intelligent Transportation Systems.

[22]  Zhen Qin,et al.  Improving multi-target tracking via social grouping , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Xudong Zhang,et al.  Graph Cut segmentation with automatic editing for Industrial images , 2010, 2010 International Conference on Intelligent Control and Information Processing.

[24]  Ramakant Nevatia,et al.  Robust Object Tracking by Hierarchical Association of Detection Responses , 2008, ECCV.

[25]  Zehang Sun,et al.  On-road vehicle detection: a review , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  J.C. Socoro,et al.  Driving assistance system based on the detection of head-on collisions , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[27]  Luc Van Gool,et al.  You'll never walk alone: Modeling social behavior for multi-target tracking , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[28]  Tao Tang,et al.  Energy-Efficient Train Tracking Operation Based on Multiple Optimization Models , 2016, IEEE Transactions on Intelligent Transportation Systems.

[29]  Xuegong Zhang,et al.  A PCA classifier and its application in vehicle detection , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[30]  Konrad Schindler,et al.  Continuous Energy Minimization for Multitarget Tracking , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Song-Chun Zhu,et al.  Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model , 2014, ECCV.

[32]  Volker Krebs,et al.  Einführung in Die Theorie des Linearen Optimalfilters (Kalman-Filter) (Introduction to Linear Optimal Filtering Theory (Kalman Filter)) , 1976, IEEE Transactions on Systems, Man and Cybernetics.

[33]  Mohan M. Trivedi,et al.  Learning to Detect Vehicles by Clustering Appearance Patterns , 2015, IEEE Transactions on Intelligent Transportation Systems.

[34]  Hui Wang,et al.  Adaptive shape prior in graph cut image segmentation , 2013, Pattern Recognit..

[35]  Luc Van Gool,et al.  The 2005 PASCAL Visual Object Classes Challenge , 2005, MLCW.

[36]  Luc Van Gool,et al.  Coupled Detection and Trajectory Estimation for Multi-Object Tracking , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[37]  Bing-Fei Wu,et al.  A New Approach to Video-Based Traffic Surveillance Using Fuzzy Hybrid Information Inference Mechanism , 2013, IEEE Transactions on Intelligent Transportation Systems.

[38]  Mubarak Shah,et al.  Abnormal crowd behavior detection using social force model , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[39]  Konrad Schindler,et al.  Discrete-continuous optimization for multi-target tracking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[40]  Sebastien Ambellouis,et al.  Vehicle detection and tracking using Mean Shift segmentation on semi-dense disparity maps , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[41]  BernardinKeni,et al.  Evaluating multiple object tracking performance , 2008 .

[42]  Volker Krebs.  Review of "Einführung in Die Theorie des Linearen Optimalfilters (Kalman-Filter)" (Introduction to Linear Optimal Filtering Theory (Kalman Filter)) by Edgar Neuburger. , 1976 .

[43]  Jianwu Fang,et al.  Adaptive road detection via context-aware label transfer , 2015, Neurocomputing.

[44]  Li-Chen Fu,et al.  Vehicle Detection under Various Lighting Conditions by Incorporating Particle Filter , 2007, 2007 IEEE Intelligent Transportation Systems Conference.

[45]  Haibin Ling,et al.  Robust Visual Tracking and Vehicle Classification via Sparse Representation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[46]  Luhong Liang,et al.  Fast car detection using image strip features , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.