Large-Scale Multi-Camera Person Trajectory Tracking Based on Low Sampling Rate of Camera

With the development of smart cities, particularly on campuses and in industrial parks, the rapid increase in the number of cameras has boosted the demand for multi-target, multi-camera tracking technology. However, current work on multi-target multi-camera tracking often involves a small number of cameras with limited monitoring coverage and short duration, insufficient for the analytical tasks required in smart parks. Moreover, existing works rely on high frame rate video streams as the data source, which, despite providing rich information, demand high storage and computational resources, limiting their application in long-term, large-scale trajectory tracking scenarios. In response to these challenges, we propose a new application scenario: large-scale multi-camera pedestrian tracking based on low-sampling-rate images. By utilizing low-sampling-rate surveillance images instead of videos and we have designed a Multi-dimensional Fusion Identity Verification architecture and a new trajectory tracking process to address the challenges brought about by large-scale camera tracking scenarios, such as environmental factors, diverse camera installation conditions, and the similarity in clothing among a large number of covered individuals. Our approach was tested in a real-world surveillance system with 2,456 cameras covering over 494.2 acres. Despite a significant reduction in data volume by over 60 times and an increase in camera count by 200 times compared to using video data sources of the real world such as WildTrack, our method still attained a pedestrian tracking accuracy of 75.8% and a recall rate of 57.4%. These results affirm our method's effectiveness and feasibility in large-scale environments.