Novelty detection in human tracking based on spatiotemporal oriented energies

Ali Emami, Mehrtash T. Harandi, Farhad Dadgostar, Brian C. Lovell

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)812-826
Number of pages15
JournalPattern Recognition
Issue number3
Publication statusPublished - 1 Mar 2015
Externally publishedYes


  • Image motion analysis
  • Novelty detection in tracking
  • Occlusion modeling
  • Spatiotemporal oriented energy
  • Video surveillance

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