SOM-based human action recognition using local feature descriptor CHOG3D

Yanli Ji, Atsushi Shimada, Hajime Nagahara, Rin Ichiro Taniguchi

Research output: Contribution to journalArticle

Abstract

Human action recognition is applied in a wide field, such as video surveillance, intelligent interface, and intelligent robots. However, since various action classes, complex surrounding, interaction with objects, et al., it is still a complex problem to be solved. In this paper, we propose a method combining the Self-Organizing Map(SOM) and the classifier k-Nearest Neighbor algorithm (k-NN) to recognize human actions. We represent human actions in the form of local features using a compact descriptor, a histogram of oriented gradient in spatio-temporal 3D space(CHOG3D), which was proposed by us in the paper 1). Then we adopt SOM for feature training to extract key features of action information. With these key features, we adopt k-NN for action recognition. In our experiments, we test the optimal map size of SOM and the proper value k of k-NN for correct recognition. Our method is tested on KTH, Weizmann and UCF datasets, and results certify its efficiency.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalResearch Reports on Information Science and Electrical Engineering of Kyushu University
Volume17
Issue number1
Publication statusPublished - May 2012

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All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering

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