Detection andmatching of multiple occludedmoving people for human tracking in colour video sequences

The proposed approach aims to track multiple moving people in a colour video acquired with a single camera. The first phase of the approach consists in precisely detecting multi-human inside moving foregrounds. The input to this phase is foreground pixels which were extracted from the scene using any background subtraction technique. These moving foregrounds are then further segmented into multiple moving people using region segmentation and shape-based occlusion handling. The second phase assigns the detected human blobs to tracks using robust matching process based both on appearance model and motion model. For this, we use Kalman filter to predict future locations and sizes for dynamic persons and fuse this information with appearance-based comparison in order to assign each blob to a track. The preliminary experiments on several representative sequences have shown that this unsupervised approach can robustly detect and track multiple occluded moving persons, even at lower temporal resolution.