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A system for learning statistical motion patterns

By W. Hu, X. Xiao, Z. Fu, D. Xie, T. Tan and Stephen J. Maybank

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 prediction

Topics: csis
Publisher: IEEE Computer Society
Year: 2006
OAI identifier: oai:eprints.bbk.ac.uk.oai2:442

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Citations

  1. (2000). A Bayesian Computer Vision System for Modeling Human Interactions,” doi
  2. (1993). A Framework for the Robust Estimation of Optical Flow,” doi
  3. (2004). A Multiobject Tracking System for Surveillance Video Analysis,” doi
  4. (1997). A State-Based Technique to the Representation and Recognition of Gesture,” doi
  5. (1999). A Survey of Fuzzy Clustering Algorithms for Pattern Recognition—Part II,” doi
  6. (2004). A Survey on Visual Surveillance of Object Motion and Behaviors,” doi
  7. (2000). A System for Video Surveillance and Monitoring,”
  8. (1991). A Validity Measure for Fuzzy Clustering,” doi
  9. (2005). Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model,” doi
  10. (2003). Activity Recognition Using the Dynamics of the Configuration of Interacting Objects,” doi
  11. (2000). Application of the Self-Organizing Map to Trajectory Classification,” doi
  12. (2003). Bayesian Human Segmentation in Crowded Situations,” doi
  13. (1999). Behaviour Model and Analysis,”
  14. (2002). Complexity Reduction for Large Image Processing,” doi
  15. (2004). Detecting Unusual Activity in Video,” doi
  16. (2000). Discovery and Segmentation of Activities in Video,” doi
  17. (2003). Double Exponential Smoothing: An Alternative to Kalman Filter-Based Predictive Tracking,” doi
  18. (2003). Expectation Grammars: Leveraging High-Level Expectations for Activity Recognition,” doi
  19. (2002). Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition,” doi
  20. (2003). Fast Accurate Fuzzy Clustering Through Data Reduction,” doi
  21. (2002). From Cluster Tracking to People Counting,”
  22. (2003). Learning a Multi-Camera Topology,” doi
  23. (2004). Learning Patterns of Activity Using Fuzzy Self-Organizing Neural Network,” doi
  24. (2000). Learning Patterns of Activity Using Real-Time Tracking,” doi
  25. (2005). Learning Semantic Scene Models from Observing Activity in Visual Surveillance,” doi
  26. (2000). Learning Spatio-Temporal Patterns for Predicting Object Behavior,” doi
  27. (1996). Learning the Distribution of Object Trajectories for Event Recognition,” doi
  28. (2000). M .O l i v e r ,B .R o s a r i o ,a n dA . P .P e n t l a n d ,“ AB a y e s i a n Computer Vision System for Modeling Human Interactions,”
  29. (2003). M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene,” doi
  30. (2002). Monitoring Crowded Traffic Scenes,” doi
  31. (2004). Multi Feature Path Modeling for Video Surveillance,” doi
  32. (2000). Multiobject Behavior Recognition by Event Driven Selective Attention Method,” doi
  33. (2004). Ontology-Driven Bayesian Networks for Dynamic Scene Understanding,” doi
  34. (1998). Parameterized Modeling and Recognition of Activities,” doi
  35. (2002). Path Detection in Video Surveillance,” doi
  36. (1998). Real-Time American Sign Language Recognition Using Desk and Wearable ComputerBased Video,” doi
  37. (2003). Recognising and Monitoring High-Level Behaviours in Complex Spatial Environments,” doi
  38. (2000). Recognition of Visual Activities and Interactions by Stochastic Parsing,” doi
  39. (2002). Reducing the Time Complexity of the Fuzzy C-Means Algorithm,” doi
  40. (1999). Representation and Recognition of Action in Interactive Spaces,”
  41. (1994). Robust Multiple Car Tracking with Occlusion Reasoning,” doi
  42. (1999). Robust Tracking of Position and Velocity with Kalman Snakes,” doi
  43. (2004). Semantic-Level Understanding of Human Actions and Interactions Using Event Hierarchy,” doi
  44. (1997). Temporal Classification of Natural Gesture and Application to Video Coding,” doi
  45. (2000). Towards Unrestricted Lip Reading,” doi
  46. (1997). Tracking Nonrigid, Moving Objects Based on Color Cluster Flow,” doi
  47. (2000). Traffic Monitoring and Accident Detection at Intersections,” doi
  48. (1996). Understanding Manipulation in Video,” doi
  49. (2003). Utilizing Learned Motion Patterns to Robustly Track Persons,”
  50. (1998). View-Based Interpretation of Real-Time Optical Flow for Gesture Recognition,” doi
  51. (1998). Visual Surveillance of Human Activity,” doi
  52. (2000). W4: Real-Time Surveillance of People and Their Activities,” doi

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