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