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Incremental activity modeling in multiple disjoint cameras.

Authors
  • Loy, Chen Change
  • Xiang, Tao
  • Gong, Shaogang
Type
Published Article
Journal
IEEE transactions on pattern analysis and machine intelligence
Publication Date
Sep 01, 2012
Volume
34
Issue
9
Pages
1799–1813
Identifiers
PMID: 22184260
Source
Medline
License
Unknown

Abstract

Activity modeling and unusual event detection in a network of cameras is challenging, particularly when the camera views are not overlapped. We show that it is possible to detect unusual events in multiple disjoint cameras as context-incoherent patterns through incremental learning of time delayed dependencies between distributed local activities observed within and across camera views. Specifically, we model multicamera activities using a Time Delayed Probabilistic Graphical Model (TD-PGM) with different nodes representing activities in different decomposed regions from different views and the directed links between nodes encoding their time delayed dependencies. To deal with visual context changes, we formulate a novel incremental learning method for modeling time delayed dependencies that change over time. We validate the effectiveness of the proposed approach using a synthetic data set and videos captured from a camera network installed at a busy underground station.

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