Online Multi-Person Tracking Using Variance Magnitude of Image colors and Solving Short Minimum Clique Problem

Multi-object tracking (MOT) is an essential but challenging task in many computer vision applications. Numerous researches have been performed on this topic where first objects are detected independently in each frame (object detection) and then the detected objects are linked together into trajectories (data association). In complex scenes, MOT is still a difficult task due to many problems including long-term occlusion by clutter or other objects, similar appearances of different objects, crowded scenes, etc. In this paper, data association is formulated as a Short Minimum Clique Problem (SMCP). Using three consecutive frames, three clusters are created where each clique between these clusters is a tracklet (partial trajectory) of a person. For this purpose, a fast and simple method is proposed for creating cliques by pruning the extra edges between clusters. For edge weights, color histogram similarities and similarity of eigenvalues of bounding boxes of people are used. Moreover for occlusion handling a trustable and fast method is applied. By saving the color histograms of people, the occlusion handling is performed. The proposed algorithm is evaluated on three challenging sequences of TUD-Crossing, TUD-Stadtmitte and PET 2009 and then compared to state-of-the-art methods where promising results are obtained.

Keywords:

Multi-object tracking , Clique , Short Minimum Clique Problem , SMCP ,