People detection is an important task in video surveillance. Due to the people’s similar characteristics and occlusion, crowded people detection for occluded classroom surveillance scenes is challenging. In this paper, a new detection framework based on the relation model method is proposed to detect crowded people in occluded classroom surveillance scenes. Our method is mainly to predict a box set of related objects and then use the positive boxes to refine the noisy boxes. Specifically, a new box set selector is designed to select positive boxes prone to generating accurate predictions, and then the rest occluded boxes are refined through the relation model module. To demonstrate the effectiveness of our proposed method, a new classroom video surveillance dataset ICDU is made, and we conduct extensive experiments on this classroom video surveillance dataset and the public dataset CrowdHuman. Experiment results show that our proposed method performs excellently on our ICDU dataset and CrowdHuman dataset.
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