A system for learning statistical motion patterns
Top Cited Papers
- 24 July 2006
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Pattern Analysis and Machine Intelligence
- Vol. 28 (9), 1450-1464
- https://doi.org/10.1109/tpami.2006.176
Abstract
Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior predictionKeywords
This publication has 42 references indexed in Scilit:
- Learning Semantic Scene Models From Observing Activity in Visual SurveillanceIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2005
- Learning Activity Patterns Using Fuzzy Self-Organizing Neural NetworkIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2004
- Multi feature path modeling for video surveillancePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Monitoring crowded traffic scenesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Path detection in video surveillanceImage and Vision Computing, 2002
- Tracking Groups of PeopleComputer Vision and Image Understanding, 2000
- TOWARDS UNRESTRICTED LIP READINGInternational Journal of Pattern Recognition and Artificial Intelligence, 2000
- Traffic monitoring and accident detection at intersectionsIEEE Transactions on Intelligent Transportation Systems, 2000
- A Bayesian computer vision system for modeling human interactionsIeee Transactions On Pattern Analysis and Machine Intelligence, 2000
- Learning the distribution of object trajectories for event recognitionImage and Vision Computing, 1996