Reduce false positives for object detection by a priori probability in videos

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• 作者：Lei WangAuthor Vitae ; Xu Zhao ; ^{zhaoxu@sjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Yuncai LiuAuthor Vitae
• 关键词：Object detection ; Deformable part models ; Pedestrian detection
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：5 October 2016
• 年：2016
• 卷：208
• 期：Complete
• 页码：325-332
• 全文大小：5954 K

文摘

In this work, we address the problem of reducing the false positives for object 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. The proposed foreground model is evaluated with the detection framework of Deformable Part Models (DPM). We combine the response of DPM detector 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 object detection by the foreground cues. Besides, 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.