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

Jargalsaikhan, Iveel, Direkoglu, Cem, Little, Suzanne orcid logoORCID: 0000-0003-3281-3471 and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2014) An evaluation of local action descriptors for human action classification in the presence of occlusion. In: International Conference on MultiMedia Modeling, 8-10 Jan 2014, Dublin, Ireland. ISBN 978-3-319-04117-9

Abstract
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 evaluate and compare the classification performance of the state-of-art human local action descriptors in the presence of varying degrees of static occlusion. We consider four different local action descriptors: Trajectory (TRAJ), Histogram of Orientation Gradient (HOG), Histogram of Orientation 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 investigate the performance of these descriptors and their possible combinations 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 partial occlusion while TRAJ in combination with MBH achieves the best results in the presence of heavy occlusion.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Human action recognition; Pattern recognition
Subjects:Computer Science > Information storage and retrieval systems
Computer Science > Multimedia systems
Computer Science > Digital video
DCU Faculties and Centres:Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: MultiMedia Modeling. Lecture Notes in Computer Science 8326. Springer. ISBN 978-3-319-04117-9
Publisher:Springer
Official URL:http://dx.doi.org/10.1007/978-3-319-04117-9_6
Copyright Information:© 2014 Springer The original publication is available at www.springerlink.com
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:European Framework Programme 7 (Grant number 285621), SFI (Insight Centre for Data Analytics)
ID Code:19714
Deposited On:28 Jan 2014 15:06 by Suzanne Little . Last Modified 19 Oct 2018 13:30
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