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In this paper, an efficient approach is proposed to improve detection efficiency of sliding window based detection methods by setting adaptive thresholds for regular object detection in the moving environment. In the proposed approach, the symmetry and variance (SYM-VAR) information of targets is learned from current frame and historical frames, and the information is used to filter out the sub-windows which may not contain the targets in the next frame. Our experimental results have demonstrated that the proposed approach can reduce nearly 50% of the average detection time with a small tradeoff of accuracy compared to typical HOG-based (histogram of oriented gradient) methods.

A Rapid Pretreatment Method For Object Detection in Dynamic Scenes