Adaptive weighted moving shadow detection based on multiple features

Abstract

Moving object detection is a fundamental task for automatic systems in video surveillance scenes, whose performance is downgraded by moving shadows unfortunately since moving objects and their shadows tend to present similar motion patterns and most moving object detection methods confused them frequently. To deal with this problem, we propose an adaptive weighted moving shadow detection method based on multiple features. In the proposed method, intensity, color and texture properties with neighboring information are exploited to generate feature maps, which are utilized to detect moving shadows respectively in small random selected videos to determine the weight for each feature map. Subsequently, the adaptive weighted fusion strategy is applied to fusion these feature maps for shadow detection according to the empirical threshold. At last, a series of spatial adjustment operations are implemented to correct misclassified pixels for obtaining refined detection results. By analyzing extensive experimental and comparison results, it demonstrates that the effectiveness and robustness of the proposed method for different video scenes and the superiority than some state-of-the-art methods.