Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33269
Title: Human action representation using pyramid correlogram of oriented gradients on motion history images
Authors: Shao, L
Zhen, X
Liu, Y 
Ji, L
Keywords: feature descriptor
human action recognition
motion history image
pyramid correlogram of oriented gradients
Issue Date: 2011
Publisher: Taylor & Francis Ltd
Source: International journal of computer mathematics, 2011, v. 88, no. 18, p. 3882-3895 How to cite?
Journal: International Journal of Computer Mathematics 
Abstract: The representation of human actions in video sequences is one of the key steps in action classification and recognition, performances of which are greatly dependent on the distinctiveness and robustness of the descriptors used for representation. In this paper, a novel descriptor, named pyramid correlogram of oriented gradients (PCOG), is presented for feature representation. PCOG, combined with the motion history images, captures both shape and spatial layout of the motion and therefore gives more effective and powerful representation for human actions and can be used for the detection and recognition of a variety of actions. Experiments on challenging action data sets show that PCOG performs significantly better than the histogram of oriented gradients both as a global descriptor and as a local descriptor.
URI: http://hdl.handle.net/10397/33269
DOI: 10.1080/00207160.2011.582102
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