A real-time system to automatically identify pedestrian meeting events from surveillance videos is proposed. The system consists of three components: a pedestrian detection and tracking module, a pedestrian group identification module and a pedestrian group record. A three level blob filter is used to improve the accuracy of pedestrian detection in the pedestrian detection and tracking module. Our previous work, the Non-recursive Motion Similarity Clustering algorithm is used as the pedestrian group identification module. Groups are detected within a time period of 0.02 ms (for 4 pedestrian in the scene) to 0.05 ms (for 32 pedestrian in the scene) of their occurrences in the video, when using a machine powered by an Intel I7 processor. The pedestrian groups identified by this algorithm are stored in Pedestrian Group Records, which are used subsequently to identify pedestrian meeting events. Visualizations are created to highlight the pedestrian groups, their history of group membership and the spatial distribution. With these visualizations, the enforcement agencies no longer need to browse through entire video archives for investigation purposes. We implemented the system to monitor several locations simultaneously in residential halls at the National University of Singapore. Our system was able to handle successfully 18 digital live video streams with resolution of 640 times 480 at 25 fps on a medium loaded Ethernet network, monitoring a maximum of 30 pedestrians and detecting 83 per cent of the meeting and split events.
More information on this research work will be added after the corresponding publications is accepted.