Incremental activity modeling in multiple disjoint cameras
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 d...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 34(2012), 9 vom: 10. Sept., Seite 1799-813 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2012
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Journal Article |
| Zusammenfassung: | 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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| Beschreibung: | Date Completed 05.02.2013 Date Revised 01.11.2012 published: Print Citation Status PubMed-not-MEDLINE |
| ISSN: | 1939-3539 |