Novelty detection in human tracking based on spatiotemporal oriented energies

Emami, Ali, Harandi, Mehrtash T., Dadgostar, Farhad and Lovell, Brian C. (2015) Novelty detection in human tracking based on spatiotemporal oriented energies. Pattern Recognition, 48 3: 812-826. doi:10.1016/j.patcog.2014.07.004

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Author Emami, Ali
Harandi, Mehrtash T.
Dadgostar, Farhad
Lovell, Brian C.
Title Novelty detection in human tracking based on spatiotemporal oriented energies
Journal name Pattern Recognition   Check publisher's open access policy
ISSN 0031-3203
Publication date 2015-03-01
Year available 2014
Sub-type Article (original research)
DOI 10.1016/j.patcog.2014.07.004
Open Access Status
Volume 48
Issue 3
Start page 812
End page 826
Total pages 15
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Collection year 2015
Language eng
Abstract Integrated analysis of spatial and temporal domains is considered to overcome some of the challenging computer vision problems such as ‘Dynamic Scene Understanding’ and ‘Action Recognition’. In visual tracking, ‘Spatiotemporal Oriented Energy’ (SOE) features are successfully applied to locate the object in cluttered scenes under varying illumination. In contrast to previous studies, this paper introduces SOE features for occlusion modeling and novelty detection in tracking. To this end, we propose a Bayesian state machine that exploits SOE information to analyze occlusion and identify the target status in the course of tracking. The proposed approach can be seamlessly merged with a generic tracking system to prevent template corruption (for example when the target is occluded). Comparative evaluations show that the proposed approach could significantly improve the performance of a generic tracking system in challenging occlusion situations.
Keyword Occlusion modeling
Novelty detection in tracking
Spatiotemporal oriented energy
Image motion analysis
Video surveillance
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online ahead of print 27 July 2014

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 1 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 1 times in Scopus Article | Citations
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Created: Tue, 23 Dec 2014, 03:18:34 EST by System User on behalf of School of Information Technol and Elec Engineering