Learning and Classification of Trajectories in Dynamic Scenes: A General Framework for Live Video Analysis

This paper presents a general framework for live video analysis. The activities of surveillance subjects are described using a spatio-temporal vocabulary learned from recurrent motion patterns. The repetitive nature of object trajectories is used to build a topographical scene description where node...

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Bibliographic Details
Main Authors: Morris, B.T., Trivedi, M.M.
Format: Conference Proceeding
Language:English
Subjects:
abnormality detection
activity prediction
Cameras
Computer vision
Laboratories
Layout
live activity analysis
Monitoring
Robot vision systems
Signal analysis
Surveillance
trajectory learning
Video compression
Videoconference
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Summary:This paper presents a general framework for live video analysis. The activities of surveillance subjects are described using a spatio-temporal vocabulary learned from recurrent motion patterns. The repetitive nature of object trajectories is used to build a topographical scene description where nodes are points of interest (POT) and the edges correspond to activity paths (AP). The POI are learned through as a mixture of Gaussians and AP by clustering trajectories. The paths are probabilistically represented by hidden Markov models and adapt to temporal variations using online maximum likelihood regression (MLLR) and through a periodic batch update. Using the scene graph, new trajectories can be analyzed in online fashion to categorize past and present activity, predict future behavior, and detect abnormalities.
DOI:10.1109/AVSS.2008.65