Abstract: With the explosive development of artificial intelligence technology, machine learning, deep learning and other technologies have been widely used in face recognition, pedestrian detection, video tracking and other fields. Among them, using target detection for indoor crowd statistics has attracted a lot of attentions. Due to the problems such as mutual occlusion of crowds and blurred target features in the indoor monitoring screen, it often leads to low detection accuracy and high false detection rate and missed detection rate. In order to solve this problem, an indoor people counting model based on global attention is proposed. The model introduces the attention mechanism, optimizes the object detection algorithm YOLOv3, and enhances the detection ability by extracting more features of small or unclear heads. The experimental results show that the improved network model has higher recall and average precision.

Key words: object detection, people counting, global attention, YOLOv3