Action recognition via an improved local descriptor for spatio-temporal features

Authors:
Kai Yang;Ji-Xiang Du;Chuan-Min Zhai
Affiliations:
Department of Computer Science and Technology, Huaqiao University, Xiamen, China;Department of Computer Science and Technology, Huaqiao University, Xiamen, China;Department of Computer Science and Technology, Huaqiao University, Xiamen, China
Venue:
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Year:
2011

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Abstract

This paper presents and investigates an improved local descriptor for spatio-temporal features on action recognition. Follow the idea of local spatio-temporal interest points on human action recognition, we develop a memory-efficient algorithm based on integral videos. The contribution of our job is we use the SURF descriptors on cuboids to speed up the computation especially for the integral video and improve the recognition rate. We present recognition results on a variety of dataset such as YouTobe and KTH, compared to previous work, the results showed that our algorithm is more efficient and accurate compared with the previous work.