Affiliations: [a] School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University, Seoul, Korea | [b] School of Journalism and Communication, Henan University, Kaifeng, Henan, China
Correspondence:
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Corresponding author: Zhiguang Li, School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University, Seoul 06974, Korea. E-mail: [email protected].
Abstract: In order to improve the tracking effect of prevention and control, this paper combines three-dimensional face recognition technology and posture recognition method to build a prevention and control tracking system, and designs a single-stage human target detection and posture estimation network based on thermal infrared images. By using infrared recognition methods, the face and human body shape can be directly recognized, overcoming the problem of traditional visual recognition methods being easily occluded. The network can simultaneously complete two tasks of human target detection and pose estimation in a multi-task manner. Moreover, this paper uses the knowledge distillation strategy to train a lightweight model to further reduce the amount of model parameters to improve the inference speed. In addition, this paper uses a single-stage human target detection and posture estimation network to judge the effect of prevention and control tracking. Through the prevention and control tracking effect test, it can be seen that the prevention and control tracking system based on 3D face recognition proposed in this paper can effectively improve the prevention and control tracking effect.
Keywords: Three-dimensional, face recognition, prevention and control, tracking
