A maximality principle applied to a contrario motion detection

A contrario modeling is a detection framework based on a perceptual grouping principle. Based on an existing a contrario motion detection method, this paper presents two solutions for improving detection results by using the confidence levels issued from the a contrario testing framework. The first one embeds the confidence levels in a maximality principle in order to operate a sensible selection among detections. This improves the description of the moving objects. The second extension enables to take into account information coming from more than one residual motion map in order to improve detection results on small slowly moving objects.