JCSE, vol. 9, no. 1, pp.29-38, January, 2015

DOI: http://dx.doi.org/10.5626/JCSE.2015.9.1.29

Improvement of Accuracy for Human Action Recognition by Histogram of Changing Points and Average Speed Descriptors

Thi Ly Vu, Trung Dung Do, Cheng-Bin Jin, Shengzhe Li, Van Huan Nguyen, Hakil Kim*, and Chongho Lee
School of Information and Communication Engineering, Inha University, Incheon, Korea

Abstract: Human action recognition has become an important research topic in computer vision area recently due to many applications in the real world, such as video surveillance, video retrieval, video analysis, and human-computer interaction. The goal of this paper is to evaluate descriptors which have recently been used in action recognition, namely Histogram of Oriented Gradient (HOG) and Histogram of Optical Flow (HOF). This paper also proposes new descriptors to represent the change of points within each part of a human body, caused by actions named as Histogram of Changing Points (HCP) and so-called Average Speed (AS) which measures the average speed of actions. The descriptors are combined to build a strong descriptor to represent human actions by modeling the information about appearance, local motion, and changes on each part of the body, as well as motion speed. The effectiveness of these new descriptors is evaluated in the experiments on KTH and Hollywood datasets.

Keyword: Human action recognition; Dense sampling; HOG; HOF; HCP; AS; SVM

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