Automatic video surveillance is of critical importance to security in commercial, law enforcement, military, and many other environments due to terrorist activity and other social problems. Generally, motion detection plays an important role as the threshold function of background and moving objects in video surveillance systems. This paper proposes a novel motion detection method with a background model module and an object mask generation module. We propose a self-adaptive background matching method to select the background pixel at each frame with regard to background model generation. After generating the adaptive background model, the binary motion mask can be computed by the proposed object mask generation module that consists of the absolute difference estimation and the Cauchy distribution model. We analyze the detection quality of the proposed method based on qualitative visual inspection. On the other hand, quantitative accuracy measurement is also obtained by using four accuracy metrics, namely, Recall, Precision, Similarity, and $F_{1}$ . Experimental results demonstrate the effectiveness of the proposed method in providing a promising detection outcome and a low computational cost.
Accurate Motion Detection Using a Self-Adaptive Background Matching Framework
IEEE Transactions on Intelligent Transportation Systems ; 13 , 2 ; 671-679
01.06.2012
1088219 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Accurate Motion Detection Using a Self-Adaptive Background Matching Framework
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