This paper presents a framework based on robust shape and appearance features for matching the various tracks generated by a single individual moving within a surveillance system. Each track is first automatically analysed in order to detect and remove the frames affected by large segmentation errors and drastic changes in illumination. The object's features computed over the remaining frames prove more robust and capable of supporting correct matching of tracks even in the case of significantly disjointed camera views. The shape and appearance features used include a height estimate as well as illumination-tolerant colour representation of the individual's global colours and the colours of the upper and lower portions of clothing. The results of a test from a real surveillance system show that the combination of these four features can provide a probability of matching as high as 91 percent with 5 percent probability of false alarms under views which have significantly differing illumination levels and suffer from significant segmentation errors in as many as 1 in 4 frames.
['Christopher S. Madden', 'Massimo Piccardi']
A framework for track matching across disjoint cameras using robust shape and appearance features