Real-Time People Counting across Spatially Adjacent Non-overlapping Camera Views

Counting the number of people traveling across non- overlapping camera views generally requires all persons exiting any camera view to be re-identified when they re-enter one of its spatially adjacent camera views. For their accurate re-identification, the correspondence among the exits and entries of all persons should be established so that their total correspondence confidence is maximized. In order to realize the real-time people counting, we propose to find the shortest time window to observe both the exits and entries of all persons traveling within the time window adaptively to the current people traffic flow. Further, since closely related people often travel together, the re-identification can be performed to the foreground regions to re-identify groups of people. Since the groups of people can sometimes split or merge outside the camera views, the proposed method establishes the weighted correspondence among the exits and entries of the foreground regions based on their correspondence confidence. Experimental results have shown that the adaptively determined time window was effective in terms of both the accuracy and the delay in people counting and the weighted correspondence was effective in terms of the accuracy especially when the people traffic gets congested and groups of people split/merge outside the camera views.