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.