Novelty detection in human tracking based on spatiotemporal oriented energies

作者：Emami Ali^*; Harandi Mehrtash T; Dadgostar Farhad; Lovell Brian C

来源：Pattern Recognition, 2015, 48(3): 812-826.

DOI：10.1016/j.patcog.2014.07.004

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

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' (SUE) features are successfully applied to locate the object in cluttered scenes under varying illumination. In contrast to previous studies, this paper introduces SUE features for occlusion modeling and novelty detection in tracking. To this end, we propose a Bayesian state machine that exploits SUE 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.

• 出版日期2015-3