Shadow detection in videos acquired by stationary and moving cameras

Abstract

Shadow Detection has become a key issue in object detection, tracking and recognition problems. Object appearances might be completely changed by the effects of shading and shadows. Finding good algorithms for shadow detection and reducing shading effects in order to segment objects from video sequences, will enhance the performance of our detection, tracking and recognition algorithms. In this thesis, we present data, physics and model-driven approaches for detecting shadows and correcting shading effects. The effectiveness of these algorithms in video sequences acquired by stationary surveillance cameras and airborne platforms is illustrated.