Cloud-Edge Architecture Spatiotemporal Enhanced Infrared Target Tracking Framework in Internet of Things

Thermal Infrared (TIR) tracking technology offers significant potential for applications in Internet of Things (IoT) systems, such as urban security and intelligent transportation systems, due to its ability to track targets in all weather conditions. However, the lack of color, texture, and other detailed features in infrared images hinders the ability of infrared trackers to effectively manage background noise and interference from similar objects. Furthermore, current deep learning-based TIR trackers face challenges when deployed in resource-limited IoT systems. To address these issues, this paper introduces a cloud-edge architecture spatiotemporal enhanced infrared target tracking framework for the Internet of Things, which enhances infrared tracking tasks from both spatial and temporal perspectives. This framework includes a Spatial Enhancement Network (SEN) designed for deployment on cloud devices to effectively improve details such as textures and edges in images, as well as a Real-time Temporal Enhancement Tracking Network (RTETN) optimized for edge device deployment, which further enhances infrared tracking performance by continuously extracting, storing, and updating temporal context. The two-stage architecture also significantly improves the flexibility, real-time capabilities, and stability of smart IoT systems while reducing maintenance costs. Experiments on two extensively used TIR target tracking datasets, LSOTB-TIR and PTB-TIR, demonstrate that our approach achieves success rates of 73.1% and 71.6%, respectively, at a speed of 46 fps, outperforming state-of-the-art algorithms in both accuracy and speed. Moreover, when using RTETN alone for tracking, it can reach a speed of 109 fps while maintaining tracking accuracy comparable to current state-of-the-art algorithms.