Abstract:
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.
