HUMAN DETECTION IN DENSE SCENE OF CLASSROOMS

The intelligent classroom project has gained a lot of attention in recent years. One of the critical applications is to recognize the position and category of an object accurately in the classrooms. Object detector based on CNN is widely used to address this issue to realize more accurate performance. But in the scene of classrooms, dense distribution of people, like students and teachers, always leads to serious class-class or class-in occlusion and brings poor accuracy to current typical detectors. To solve this problem, we propose an original method named Dense Occlusion Object Detection network, which consists of Dense Anchor Generation Model (DAG) and Discriminative Part Selection Model (DPS). The DAG model could exploit crucial semantic information from feature map to generate accurate anchor boxes by locating center point and predicting box size of an object in the anchor-free manner. The DPS model aims to mitigate the occlusion problem through dividing the proposal into some smaller parts and processing them respectively so that we can select the most discriminative ones to update the confidence score of the input proposal. Experimental results show that the proposed method outperforms the state-of-the-art methods for human detection on our CScene dataset.