Efficient Multi Object Tracking for Learning in IoT Monitoring Environment

In the Internet of Things (IoT) monitoring environment, multi-target tracking technology is crucial to achieve real-time monitoring, behavior analysis and event warning in complex environments. However, existing technologies have limitations in handling complex scenes such as occlusion, similar appearance, and fast-moving targets, which severely limits the practicality and accuracy of multi-object tracking technology. This paper studies an efficient multi-target tracking technology based on deep learning and data association optimization. Through modular design, key steps such as data preprocessing, target detection, feature extraction, target association, trajectory prediction and update are integrated to build a comprehensive research framework. Experimental results show that compared with existing methods, the proposed method reaches the maximum value of 0.811 and 0.839 in the accuracy and precision indicators of multi-target tracking, respectively, and achieves 100% target recapture rate, which is significant. Improved tracking accuracy and stability. Although there is still room for improvement in performance under extremely small targets and irregular motion modes, the technical solution proposed in this study provides an efficient and reliable solution for multi-target tracking in IoT monitoring environments, showing a wide range of application prospects. and actual value.