Multi-Object Tracking Using Common Eigenvalues and the Short Minimum Clique Problem

Data association is the main part of many multi-object tracking (MOT) methods and is inherently prone to problems such as ID-switches and difficulties caused by long-term occlusion, cluttered background, and crowded scenes. 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 common 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 done. The tracker is evaluated on five challenging sequences of TUD-Crossing, TUD-Stadtmitte, PET 2009, ETH SUNNYDAY and Parking Lot 1 and then compared to state-of-the-art methods where promising results are obtained.

کلیدواژه ها:

Data Association ، Clique ، Short Minimum Clique Problem ، multi-object tracking