Identifying moving objects from a video scene is a fundamental and critical task in object tracking. However, shadows extracted along with the objects can result in large errors in object localization and recognition. Despite many attempts, the problem remains largely unsolved due to several challenges. Since cast shadows can be as big as the actual objects, their incorrect classification as foreground results in inaccurate detection and decreases tracking performance. Hence, an effective method for shadow detection and removal is required significantly to provide urgent support and to reduce the effects of incorrect object tracking.
In this paper, an efficient method for removing cast shadow from vehicles is proposed. The method works by applying a Gamma decoding followed by a thresholding operation and employing the estimated background model of the video sequence. A number of experiments has been performed. The results revealed the proposed algorithm is efficient and leading to improved tracking process