Deviation Control Theory and Strategy in Aluminum Hot Rolling

Fu Zhuo, Liu Yilun, Ma Liyong


Real-time detection and tracking of moving pedestrians in image sequences is a fundamental task in many computation vision applications such as automated visual surveillance system. In this paper we propose a human detection method based on foreground segmentation, and the detection speed is satisfying for the application of video surveillance. During detection, unlike the exhaustive scan typically used in general human detection systems, in order to avoid scanning regions like the sky, the foreground segmentation stage is firstly implemented in the video surveillance sequences by utilizing Gaussian mixture model algorithm, and then, human detection stage is executed on the regions of interest (ROI) extracted from the video surveillance sequence frame. In contrast with the exhaustive scan without explicit segmentation, our proposed approach can meet the real-time requirement. Carefully designed experiments demonstrate the superiority of our proposed approach.



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