Autonomous and Adaptive Learning of Shadows for Surveillance

Object detection is a critical step in automating the monitoring and surveillance tasks. To maximize its reliability, robust algorithms are needed to separate real objects from moving shadows. In this paper we propose a framework for detecting moving shadows caused by moving objects in video, which first learns autonomously and on-line the characteristic features of typical shadow pixels at various parts of the observed scene. The collected knowledge is then used to calibrate itself for the given scene, and to identify shadow pixels in subsequent frames. Experiments show that our system has a good performance, while being more adaptable and using only brightness information.