Design of efficient human head statistics system in the large-angle overlooking scene

Abstract

Human head statistics is widely used in the construction of smart cities and has great market value. In order to solve the problem of missing pedestrian features and poor statistics results in a large-angle overlooking scene, in this paper we propose a human head statistics system that consists of head detection, head tracking and head counting, where the proposed You-Only-Look-Once-Head (YOLOv5-H) network, improved from YOLOv5, is taken as the head detection benchmark, the DeepSORT algorithm with the Fusion-Hash algorithm for feature extraction (DeepSORT-FH) is proposed to track heads, and heads are counted by the proposed cross-boundary counting algorithm based on scene segmentation. Specifically, Complete-Intersection-over-Union (CIoU) is taken as the loss function of YOLOv5-H to make the predicted boxes more in line with the real boxes. The results demonstrate that the recall rate and mAP@.5 of the proposed YOLOv5-H can reach up to 94.3% and 93.1%, respectively, on the SCUT_HEAD dataset. The statistics system has an extremely low error rate of 3.5% on the TownCentreXVID dataset while maintaining a frame rate of 18FPS, which can meet the needs of human head statistics in monitoring scenarios and has a good application prospect.