Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kasturi, R., Goldgof, D., Soundararajan, P., Manohar, V., Garofolo, J., Bowers, R., Boonstra, M., Korzhova, V., Zhang, J.: Framework for performance evaluation of face, text, and vehicle detection and tracking in video: data, metrics, and protocol. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 319–336 (2009)
Di Caterina, G., Soraghan, J. J.: An improved mean shift tracker with fast failure recovery strategy after complete occlusion. In: IEEE International Conference on Advanced Video and Signal-Based Surveillance, pp. 130–135 (2011)
Gao, J.: Self-occlusion immune video tracking of objects in cluttered environment. In: IEEE International Conference on Advanced Video and Signal-Based Surveillance, pp. 79–84 (2003)
PETS2009: Benchmark data. http://www.cvg.rdg.ac.uk/PETS2009/a.html
ETISEO: Evaluation For Video Understanding. http://www-sop.inria.fr/orion/ETISEO/
Taylor, G.R., Chosak, A.J., Brewe, P.C.: OVVV: using virtual worlds to design and evaluate surveillance systems. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. In: ACM Computing Surveys, vol. 38, no. 4 (2006)
Comaniciu, D., Ramesh, V.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)
Comaniciu, D., Ramesh, V., Meer P.: Real-time tracking of non-rigid objects using mean shift. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 142–149 (2000)
Yilmaz, A.: Object tracking by asymmetric Kernel mean shift with automatic scale and orientation selection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, CVPR ‘07, pp. 1–6 (2007)
Mirabi, M., Javadi, S.: People tracking in outdoor environment using Kalman filter. In: Proceedings of Third International Conference on Intelligent Systems, Modelling and Simulation (ISMS), pp. 303–307 (2012)
Wang, H., Huo, L., Zhang, J.: Target tracking algorithm based on dynamic template and Kalman filter. In: Communication Software and Networks (ICCSN), 2011 IEEE 3rd, pp. 330–333 (2011)
Cho, J.U., Jin, S.H., Pham, X.D., Jeon, J.W.: Object tracking circuit using particle filter with multiple features. In: SICE-ICASE, 2006. International Joint Conference, pp. 1431–1436 (2006)
Liang, N., Guo, L., Wang Y.: An improved object tracking method based on particle filter. In: Consumer Electronics, Communications and Networks (CECNet), 2012 2nd International Conference on, pp. 3107–3110 (2012)
Li, Y., Huang, C., Nevatia, R.: Learning to associate: hybrid boosted multi-target tracker for crowded scene. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2953–2960 (2009)
Manohar, V., Soundararajan, P., Dmitry Goldgof, H.R., Kasturi, R., Garofolo, J.: Performance evaluation of object detection and tracking in video. In: Narayanan P.J. et al. (eds.) ACCV 2006. LNCS, vol. 3852, pp. 151–161. Springer, Heidelberg (2006)
Kashanipour, A.: 2D Target Tracking Using Kalman Filter, MATLAB Central. http://www.mathworks.com/matlabcentral/fileexchange/14243-2d-target-tracking-using-kalman-filter
Paris, S.: Particle filter color tracker. In: MATLAB Central. http://www.mathworks.com/matlabcentral/fileexchange/17960-particle-filter-color-tracker
Bernhard, S.: Mean-Shift Video Tracking, MATLAB Central. http://www.mathworks.com/matlabcentral/fileexchange/355, March 2012
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media Dordrecht
About this paper
Cite this paper
Lee, B.Y., Liew, L.H., Cheah, W.S., Wang, Y.C. (2012). Simulation Videos for Understanding Occlusion Effects on Kernel Based Object Tracking. In: Yeo, SS., Pan, Y., Lee, Y., Chang, H. (eds) Computer Science and its Applications. Lecture Notes in Electrical Engineering, vol 203. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5699-1_15
Download citation
DOI: https://doi.org/10.1007/978-94-007-5699-1_15
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-5698-4
Online ISBN: 978-94-007-5699-1
eBook Packages: Computer ScienceComputer Science (R0)