Learning Motion Patterns in Surveillance Video using HMM Clustering
- 1 January 2008
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
We present a novel approach to learning motion behavior in video, and detecting abnormal behavior, using hierarchical clustering of hidden Markov models (HMMs). A continuous stream of track data is used for online and on-demand creation and training of HMMs, where tracks may be of highly variable length and scenes may be very complex with an unknown number of motion patterns. We show how these HMMs can be used for on-line clustering of tracks that represent normal behavior and for detection of deviant tracks. The track clustering algorithm uses a hierarchical agglomerative HMM clustering technique that jointly determines all the HMM parameters (including the number of states) via an expectation maximization (EM) algorithm and the Akaike information criteria. Results are demonstrated on a highly complex scene containing dozens of routes, significant occlusions and hundreds of moving objects.Keywords
This publication has 13 references indexed in Scilit:
- Multi-Object Tracking Through Simultaneous Long Occlusions and Split-Merge ConditionsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- "Shape Activity": a continuous-state HMM for moving/deforming shapes with application to abnormal activity detectionIEEE Transactions on Image Processing, 2005
- Video behaviour profiling and abnormality detection without manual labellingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Event semantics in two-person interactionsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Estimating Tracking Sources and SinksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Path detection in video surveillanceImage and Vision Computing, 2002
- Classifying Surveillance Events from Attributes and BehaviourPublished by British Machine Vision Association and Society for Pattern Recognition ,2001
- Learning patterns of activity using real-time trackingIeee Transactions On Pattern Analysis and Machine Intelligence, 2000
- Clustering sequence data using hidden Markov model representationPublished by SPIE-Intl Soc Optical Eng ,1999
- A tutorial on hidden Markov models and selected applications in speech recognitionProceedings of the IEEE, 1989