Three camera-based human tracking using weighted color and cellular LBP histograms in a particle filter framework

In this paper an effective three view multiple human tracking method based on color and texture information fusion is proposed. Since human motion is usually non-linear and non-Gaussian, a particle filter framework is used to estimate human position. Human model is jointly represented by weighted color and cellular LBP (cellular local binary pattern) histograms. Weighted color histogram is robust to scale invariant and partial occlusion but has a main limitation when object's color and background's color are similar; so using these two complement features improve tracking results. This method is robust against illumination changes and occlusions. A three-camera network is used to handle occlusion. Tracking process has done separately for each camera, when occlusion is detected in one view. Tracking results of two other views are used to handle occlusion. Experimental results demonstrate that the proposed method improves performance of human tracking.

[1]  Ting-zhi Shen,et al.  Face tracking using multiple facial features based on particle filter , 2010, 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010).

[2]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[3]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[4]  T. Higuchi.  Monte carlo filter using the genetic algorithm operators , 1997 .

[5]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[6]  Luc Van Gool,et al.  An adaptive color-based particle filter , 2003, Image Vis. Comput..

[7]  Cedric Nishan Canagarajah,et al.  Sequential Monte Carlo tracking by fusing multiple cues in video sequences , 2007, Image Vis. Comput..

[8]  Matti Pietikäinen,et al.  Multi-Object Tracking Using Color, Texture and Motion , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

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

[10]  Rama Chellappa,et al.  Robust two-camera tracking using homography , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  ZhaoHui Zhang,et al.  Particle-filter-based object tracking with color and texture information fusion , 2009, International Symposium on Multispectral Image Processing and Pattern Recognition.

[12]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[13]  Yong Wang,et al.  Adaptive hybrid likelihood model for visual tracking based on Gaussian particle filter , 2010 .

[14]  Patrick Pérez,et al.  Data fusion for visual tracking with particles , 2004, Proceedings of the IEEE.