Abstract
This paper presents two robust algorithms with respect to global contrast changes: one detects changes; and the other detects stationary people or objects in image sequences obtained via a fixed camera. The first one is based on a level set representation of images and exploits their suitable properties under image contrast variation. The second makes use of the first, at different time scales, to allow discriminating between the scene background, the moving parts and stationarities. This latter algorithm is justified by and tested in real-life situations; the detection of abnormal stationarities in public transit settings, e.g. subway corridors, will be presented herein with assessments carried out on a large number of real-life situations.
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