Human tracking with multiple 3D cameras for perceptual sensor network

In this paper, we propose a multiple 3D camera-based human tracking method which is robust to illumination changes and occlusions at indoor environments. To overcome the difficulties due to illumination change, several types of image features are used in a collaborative fashion, for which brightness intensity, hue, local binary pattern (LBP) and depth from 3D camera are considered. In addition, our method also exploits multiple camera views to resolve the occlusion between objects. Our algorithm first implements the background subtraction to extract moving objects from each camera view and then executes the human identification process to determine whether the human is previously confirmed. The proposed algorithm estimates the vertical axes of the humans detected in multiple calibrated camera views, which leads to generating the cross points of the detected human objects. Finally, the cross points (the location of the human objects) are fed into adaptive particle filter based on spatio-temporal information to track the human objects. The performance of the proposed algorithm is examined through experiments performed in varying indoor illumination and occlusion conditions.