Skip to main content
Article thumbnail
Location of Repository

A Neural System for Automated CCTV Surveillance

By Andrew Hunter, Jonathan Owens and M. Carpenter


This paper overviews a new system, the “Owens\ud Tracker,” for automated identification of suspicious\ud pedestrian activity in a car-park.\ud Centralized CCTV systems relay multiple video streams\ud to a central point for monitoring by an operator. The\ud operator receives a continuous stream of information,\ud mostly related to normal activity, making it difficult to\ud maintain concentration at a sufficiently high level.\ud While it is difficult to place quantitative boundaries on\ud the number of scenes and time period over which\ud effective monitoring can be performed, Wallace and\ud Diffley [1] give some guidance, based on empirical and\ud anecdotal evidence, suggesting that the number of\ud cameras monitored by an operator be no greater than 16,\ud and that the period of effective monitoring may be as\ud low as 30 minutes before recuperation is required.\ud An intelligent video surveillance system should\ud therefore act as a filter, censuring inactive scenes and\ud scenes showing normal activity. By presenting the\ud operator only with unusual activity his/her attention is\ud effectively focussed, and the ratio of cameras to\ud operators can be increased.\ud The Owens Tracker learns to recognize environmentspecific\ud normal behaviour, and refers sequences of\ud unusual behaviour for operator attention. The system\ud was developed using standard low-resolution CCTV\ud cameras operating in the car-parks of Doxford Park\ud Industrial Estate (Sunderland, Tyne and Wear), and\ud targets unusual pedestrian behaviour.\ud The modus operandi of the system is to highlight\ud excursions from a learned model of normal behaviour in\ud the monitored scene. The system tracks objects and\ud extracts their centroids; behaviour is defined as the\ud trajectory traced by an object centroid; normality as the\ud trajectories typically encountered in the scene. The\ud essential stages in the system are: segmentation of\ud objects of interest; disambiguation and tracking of\ud multiple contacts, including the handling of occlusion\ud and noise, and successful tracking of objects that\ud “merge” during motion; identification of unusual\ud trajectories. These three stages are discussed in more\ud detail in the following sections, and the system\ud performance is then evaluated

Topics: G760 Machine Learning
Year: 2003
OAI identifier:

Suggested articles


  1. A Fast Model-Free Morphology-Based Object Tracking Algorithm,” doi
  2. (1998). A Real-Time System for Video Surveillance of Unattended Outdoor Environments”, doi
  3. (1999). A Topological and Temporal Correlator Network for Spatiotemporal Pattern Learning, Recognition and Recall”, doi
  4. Application of the SelfOrganising Map to Trajectory Classification”, doi
  5. (1998). CCTV: Making it Work,” Police Scientific Development Branch of the Home Office (PSDB) publication 14/98,
  6. (1996). Comparison of Background Extraction Based Intrusion Detection Algorithms.” doi
  7. (1999). Image Processing System for Pedestrian Monitoring Using Neural Classification of Normal Motion Patterns”, doi
  8. Learning and Classification of Suspicious Events for Advanced Visual-Based Surveillance.” In: “Multimedia Video-Based Surveillance Systems: Requirements, doi
  9. (1991). Learning Invariance from Transformation Sequences”, doi
  10. (2000). Learning Patterns of Activity Using Real-Time Tracking.” doi
  11. (1996). Learning the Distribution of Object Trajectories for Event Recognition”, doi
  12. Novelty Detection in Video Surveillance Using Hierarchical Neural Networks”, doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.