A Novel Shadow Detection Algorithm for Real Time Visual Surveillance Applications

A common problem that one could encounter in motion estimation of indoor, or yet more, of daytime outdoor scenes is that of the detection of shadows attached to their respective moving objects. The detection of a shadow as a legitimate moving region may mislead an algorithm for the subsequent phases of analysis and tracking, which is why moving objects should be separated from their shadow. This paper presents work we have done to detect moving shadows in gray level scenes in real time for visual surveillance purposes. In this work we do not rely on any a priori information regarding with color, shape or motion speed to detect shadows. Rather, we exploit some statistical properties of the shadow borders after they have been enhanced through a simple edge gradient based operation. We developed the overall algorithm using a challenging outdoor traffic scene as a “training” sequence. Secondly, we assess the effectiveness of our shadow detection method by extracting the ground truth from gray level sequences taken indoors and outdoors from different urban and highway traffic scenes.