An improved method of action recognition based on sparse spatio-temporal features

Authors:
Junwei Zhu;Jin Qi;Xiangbin Kong
Affiliations:
College of Electric Engineering, University of Electronic Science and Technology of China, Chengdu, China;College of Electric Engineering, University of Electronic Science and Technology of China, Chengdu, China;College of Electric Engineering, University of Electronic Science and Technology of China, Chengdu, China
Venue:
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
Year:
2012

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Abstract

Sparse, informative feature representation has become an extremely successful method in action feature detection. Such features make the task more manageable while providing increased robustness to noise and pose variation. As the feature points detected are numerous, thus affect the computational efficiency. In this paper we present an improvement of this idea by decreasing the number of key points. And then we combine the use of 3D SIFT and pLSA in action categories. To test the validation of our method, we show the framework we devise in detail, also present some behavior recognition results on the KTH dataset including boxing, handclapping, hand waving, walking, jogging, and running.