A neural system for automated CCTV surveillance
Andrew Hunter · 2003
We present the Owens Tracker, a complete hybrid neural prefiltering system for tracking pedestrians in a car park, and raising operator attention when unusual activity (defined by pedestrian trajectories) is detected. The system uses a combination of background differencing to detect moving objects, a specialized multiple tracking algorithm to maintain object records, and a two-part neural novelty detection module to detect novel trajectories defined both by short-term and long-term characteristics. The system was developed using data from a commercial industrial park. Experiments demonstrate that it is very robust; it detects and discounts the movement of cars, and can handle problems such as car drop-offs, noise, shadows and reflections. It reliably detected virtually all unusual pedestrian trajectories, but raised a number of false positive alarms. Approximately 50% of these were due to tracking failures, indicating that some improvement in that component of the system would be useful; the others are tolerable, given the system's target deployment as an attention-focussing filter. However, even in its current form, the system has the potential to reduce dramatically the burden of user monitoring, as only about 20 minutes footage from ten hours was identified as needing operator attention - a 30-fold decrease in effort. (5 pages)