Segmentation and Tracking Framework for Video Object by Using Threshold Decision and Diffusion
نویسندگان
چکیده
Video object segmentation and tracking are two essential building blocks of smart surveillance systems. However, there are several issues that need to be resolved. Threshold decision is a difficult problem for video object segmentation with a multibackground model.Addition to , some conditions make robust video object tracking difficult. These conditions include nonrigid object motion, target appearance variations due to changes in illumination, and background clutter. In this paper, a video object segmentation and tracking framework is proposed for smart cameras in visual surveillance networks with two major contributions. First, propose a robust threshold decision algorithm for video object segmentation with a multibackground model. Second, a new technic is proposed a video object tracking framework based on a particle filter with the likelihood function composed of diffusion distance for measuring color histogram similarity and motion clue from video object segmentation. The proposed framework can track nonrigid moving objects under drastic changes in illumination and background clutter. Experimental results show that the presented algorithms perform well for several challenging sequences, and the proposed methods are effective for the aforementioned issues. Keywords— Diffusion distance (DD), particle filter, smart camera, surveillance, threshold decision, tracking.
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