A robust adaptive algorithm of moving object detection for video surveillance

In visual surveillance of both humans and vehicles, a video stream is processed to characterize the events of interest through the detection of moving objects in each frame. The majority of errors in higher-level tasks such as tracking are often due to false detection. In this paper, a novel method is introduced for the detection of moving objects in surveillance applications which combines adaptive filtering technique with the Bayesian change detection algorithm. In proposed method, an adaptive structure firstly detects the edges of motion objects. Then, Bayesian algorithm corrects the shape of detected objects. The proposed method exhibits considerable robustness against noise, shadows, illumination changes, and repeated motions in the background compared to earlier works. In the proposed algorithm, no prior information about foreground and background is required and the motion detection is performed in an adaptive scheme. Besides, it is shown that the proposed algorithm is computationally efficient so that it can be easily implemented for online surveillance systems as well as similar applications.