Reduce false positives for human detection by a priori probability in videos
Lei Wang, Xu Zhao, Yuncai Liu · 2015
In this work, we address the problem of reducing the false positives for human detection in videos. We employ the motion cue to build a foreground probability model. Then the mean expectation of the pixel-level foreground probability is computed to assign a priori probability to the sliding window in detection. We combine the response of Deformable Part Models and the mean probability expectation to form the features and train a linear classifier. The proposed approach is threshold-free, and reduces the false positives in human detection by the foreground cues. As well, we describe an integral probability image for fast computation of the mean probability expectation. Experimental results show that the proposed method achieve superior performance over the baseline of Deformable Part Models.