Transfer Learning-Based Security Protection Framework for Smart Exercise Monitoring

Abstract

Sports exercise is very important for both physical and mental health, but improper exercise or equipment use may cause some security challenges. To address this issue, facing Internet of Things (IoT), currently, some video recognition algorithm-based smart security monitoring systems have been designed. However, existing video recognition algorithms usually assume that the data comes from the same collection device or follows the same distribution, resulting in ineffective handling of cross-camera or cross-device recognition problems. In this vein, this paper designed a transfer learning-based smart exercise monitoring security protection system, and proposed a new transfer learning-based video recognition framework, which consists of backbone network module, style transfer module, video feature aggregation module three parts and using this framework, two different models can be trained based on video face recognition dataset and video action recognition dataset, respectively, for identity recognition and action recognition. Experimental results show that the proposed framework can effectively handle video face recognition and video action recognition problems, which also demonstrates that our designed smart exercise monitoring security protection system can meet actual task requirements.