LSTM and GRU-Based Lightweight Temporal Models for Edge Computing on Real-Time Individual Abnormal Behavior Recognition

Techniques of abnormal behavior recognition enhance public safety by analyzing the vision-based biometric features of the individual. However, main-stream deep learning-based approaches heavily rely on hardware computility, which is difficult to deploy on edge devices. This defect harms their practical integrity as a biometric identification system and the potential for commercial application. We introduce a novel lightweight framework for edge-based real-time anomaly detection by combining lightweight MobileInst architecture with temporal modules namely MobLSTM and MobGRU. The MobLSTM improves capability of detecting gradual or nuanced abnormal activities by incorporating temporal LSTM model. And the MobGRU simplifies information flow from backbone to minimize the computational burden. The Query-Passing Mechanism is also adapted to further reduce computation redundancy by reusing queries obtained from consecutive frames. The approach is evaluated on UCF-Crime dataset, a benchmark widely used in abnormal behavior detection. Experiment results indicate MobLSTM yields a frame-level anomaly detection accuracy of around 92.82% at 90.8 FPS and MobGRU with the accuracy of 89.59% at 92.3 FPS. These findings further highlight the practicality of deploying high performance anomaly detection systems in resourceconstrained environments, presenting a scalable solution for real-world surveillance, and demonstrating significant implications in the improvement of public safety via conveniently available real-time biometric identification systems.