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An evaluation of local action descriptors for human action classification in the presence of occlusion


Iveel Jargalsaikhan, Cem Direkoglu, Suzanne Little, Noel O'Connor

Publication Type: 
Refereed Conference Meeting Proceeding
This paper examines the impact that the choice of local de- scriptor has on human action classifier performance in the presence of static occlusion. This question is important when applying human action classification to surveillance video that is noisy, crowded, complex and incomplete. In real-world scenarios, it is natural that a human can be occluded by an object while carrying out different actions. However, it is unclear how the performance of the proposed action descriptors are affected by the associated loss of information. In this paper, we evalu- ate and compare the classification performance of the state-of-art human local action descriptors in the presence of varying degrees of static oc- clusion. We consider four different local action descriptors: Trajectory (TRAJ), Histogram of Orientation Gradient (HOG), Histogram of Ori- entation Flow (HOF) and Motion Boundary Histogram (MBH). These descriptors are combined with a standard bag-of-features representation and a Support Vector Machine classifier for action recognition. We in- vestigate the performance of these descriptors and their possible com- binations with respect to varying amounts of artificial occlusion in the KTH action dataset. This preliminary investigation shows that MBH in combination with TRAJ has the best performance in the case of par- tial occlusion while TRAJ in combination with MBH achieves the best results in the presence of heavy occlusion.
Conference Name: 
International Conference on MultiMedia Modeling
International Conference on MultiMedia Modeling
Digital Object Identifer (DOI): 
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Conference Location: 
Research Group: 
Dublin City University (DCU)
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