Simulation Videos for Understanding Occlusion Effects on Kernel Based Object Tracking

Occlusion handling is one of the most studied problems for object tracking in computer vision. Many previous works claimed that occlusion can be handled effectively using Kalman filter, Particle filter and Mean Shift tracking methods. However, these methods were only tested on specific task videos. In order to explore the actual potential of these methods, this paper introduced 64 simulation video sequences to experiment the effectiveness of each tracking methods on various occlusion scenarios. Tracking performances are evaluated based on Sequence Frame Detection Accuracy (SFDA). The results showed that Mean shift tracker would fail completely when full occlusion occurred. Kalman filter tracker achieved highest SFDA score of 0.85 when tracking object with uniform trajectory and no occlusion. Results also demonstrated that Particle filter tracker fails to detect object with non-uniform trajectory. The effect of occlusion on each tracker is analyzed with Frame Detection Accuracy (FDA) graph.

[1]  Dmitry B. Goldgof,et al.  Performance Evaluation of Object Detection and Tracking in Video , 2006, ACCV.

[2]  Ram Nevatia,et al.  Learning to associate: HybridBoosted multi-target tracker for crowded scene , 2009, CVPR.

[3]  Andrew J. Chosak,et al.  OVVV: Using Virtual Worlds to Design and Evaluate Surveillance Systems , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Jing Zhang,et al.  Target tracking algorithm based on dynamic template and Kalman filter , 2011, 2011 IEEE 3rd International Conference on Communication Software and Networks.

[5]  Alper Yilmaz,et al.  Object Tracking by Asymmetric Kernel Mean Shift with Automatic Scale and Orientation Selection , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Jae Wook Jeon,et al.  Object Tracking Circuit using Particle Filter with Multiple Features , 2006, 2006 SICE-ICASE International Joint Conference.

[7]  Ying Wang,et al.  An improved object tracking method based on particle filter , 2012, 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet).

[8]  John J. Soraghan,et al.  An improved Mean Shift tracker with fast failure recovery strategy after complete occlusion , 2011, 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[9]  Jing Zhang,et al.  Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  S. Javadi,et al.  People Tracking in Outdoor Environment Using Kalman Filter , 2012, 2012 Third International Conference on Intelligent Systems Modelling and Simulation.

[12]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[13]  Jean Gao.  Self-occlusion immune video tracking of objects in cluttered environments , 2003, Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003..