Using Cumulative Histogram Maps in an Adaptive Color-Based Particle Filter for Real-Time Object Tracking

Abstract:

The study presents a human tracking system. To tracking a person, we adopt a particle filter as tracking kernel, since the method has proven successful for tracking in non-linear and non-Gaussian estimation. In a particle filter, a set of weighted particles represents the possible target sates. In this study, we measure the weight according to both the appearances of the target object and background scene to improve the discriminability between them. In our tracker, the appearances are modeled as color histogram, since it is scale and rotation invariant. However, the color histogram extraction for a large number of overlap regions is repeated redundantly and inefficiently. To speed up it, we reduce the cost for calculating overlapped regions by creating a cumulative histogram map for the processing image. The experimental results show that the tracker has the best precision improvement, and the tracking speed is 49.7 fps for 384 × 288 resolution, when we use 600 particles. The results show that the proposed method can be applied to a real-time human tracking system with high precision.

• Periodical: Advanced Materials Research (Volumes 121-122)
• Pages: 585-590
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.121-122.585
• Online since: June 2010
• Authors: San Lung Zhao, Shen Zheng Wang, Hsi Jian Lee, Hung I Pai
• Keywords: Background Model, Human Tracking, Particle Filter (PF)

References

[1] D. Comaniciu, V. Ramesh, and P. Meer, Kernel-Based Object Tracking, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp.564-575, May (2003).

DOI: 10.1109/tpami.2003.1195991

[2] K. Nummiaro, E. Koller-Meier, and L. V. Gool, An adaptive color-based particle filter, Image and Vision Computing, vol. 21, no. 1, pp.99-110, Jan. (2003).

DOI: 10.1016/s0262-8856(02)00129-4

[3] C. Stauffer and W. E. L. Grimson, Learning patterns of activity using real-time tracking, IEEE Trans. Pattern Analysis and Machine Intelligence , vol. 22, no. 8, pp.747-757, Aug. (2000).

DOI: 10.1109/34.868677

[4] C. Shan, T. Tan, and Y. Wei, Real-time hand tracking using a mean shift embedded particle filter, Pattern Recognition, vol. 40, no. 7, pp.1958-1970, Jul. (2007).

DOI: 10.1016/j.patcog.2006.12.012

[5] E. Polat, M. Yeasin, and R. Sharma, Robust Tracking of Human Body Parts for Collaborative Human Computer Interaction, Computer Vision and Image Understanding, vol. 89, no. 1, pp.44-69, Jan. (2003).

DOI: 10.1016/s1077-3142(02)00031-0

[6] C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland, Pfinder: Real-Time Tracking of the Human Body, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp.780-785, Jul. (1997).

DOI: 10.1109/34.598236

[7] M. Isard and A. Blake, CONDENSATION - Conditional Density Propagation for Visual Tracking, " Int, l J. Computer Vision, vol. 29, no. 1, pp.5-28, Aug. (1998).

[8] M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Trackin, IEEE Trans. Signal Processing, vol. 50, no. 2, pp.174-188, Feb. (2002).

DOI: 10.1109/78.978374

[9] P. Pérez, C. Hue, J. Vermaak, and M. Gangnet, Color-Based Probabilistic Tracking, Proc. 7th European Conf. Computer Vision, vol. 1, Copenhagen, Denmark, 2002, pp.661-675.

DOI: 10.1007/3-540-47969-4_44

[10] P. Viola and M. Jones, Rapid Object Detection using a Boosted Cascade of Simple Features, Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, vol. 1, Los Alamitos, CA, USA, 2001, pp.511-518.

DOI: 10.1109/cvpr.2001.990517

[11] CAVIAR. Information on http: /www. dai. ed. ac. uk/homes/rbf/CAVIAR.