Multi-Camera People Tracking With Spatio-Temporal and Group Considerations

Multi-camera people tracking (MCPT) has become increasingly relevant in recent years as the demand for accurate people-tracking systems has increased along with the growing number of surveillance cameras. There are challenges that MCPT must overcome, such as changes in a person's appearance due to illumination and changes in viewpoint and posture between cameras. Additionally, occlusion is likely to occur in crowded places like tourist spots, making it even more difficult to perform accurate tracking based on a person's appearance. To address these problems, we propose an MCPT system that uses additional spatio-temporal and group information. First, the system uses spatio-temporal filtering to remove candidates that are considered irrelevant. Then, group-aware matching is used to correct ID matching errors based solely on the features of an individual's appearance. In this paper, we evaluate this system on data collected from surveillance cameras at Kumamoto Castle, a tourist spot designated as a National Important Cultural Property. This dataset contains images of people in a wide age range and with various degrees of crowding. We demonstrate that our system is effective in scenarios with large crowds, and that the additional contextual information is helpful when it is difficult to track based on a person's appearance alone.