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IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance Learning and Classification of Trajectories in Dynamic Scenes: A General Framework for Live Video Analysis

By Brendan T. Morris and Mohan M. Trivedi

Abstract

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 (POI) 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. 1

Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.352.4918
Provided by: CiteSeerX
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