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Abstract
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.
Highlights
1 Introduction Today, stationary cameras are extensively used for video surveillance systems [1]
A typical surveillance system consists of three building blocks: moving object detection, object tracking and higher-level motion analysis [2]
There is a list of challenging problems in the video surveillance applications addressed including illumination changes, repeated motions of background, bootstrapping, and shadows [17]
Summary
Stationary cameras are extensively used for video surveillance systems [1]. Visual surveillance is employed in many applications, such as car and pedestrian traffic monitoring, human activity surveillance for unusual activity detection, people counting, etc. 4.2 Proposed ANC-MAP detection algorithm Having some primary frames with no moving object, a background model may be available. It should be able to quickly adapt to background changes (for example starting and stopping of vehicles) To cope with these problems, successive frames are selected to be applied to the proposed ANC-based algorithm in spite of better performance when a background model is used. Quantitative evaluation and comparison with the existing methods show that the proposed method provides better performance
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