Recognizing human actions using curvature estimation and NWFE-based histogram vectors

Fu Song Hsu*, Cheng Hsien Lin, Wei Yang Lin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This paper presents a novel scheme for human action recognition. First of all, we employ the curvature estimation to analyze human posture patterns and to yield the discriminative feature sequences. The feature sequences are further represented into sets of strings. Consequently, we can solve human action recognition problem by the string matching technique. In order to boost the performance of string matching, we apply the NonparametricWeighted Feature Extraction (NWFE) to compact the string representation. Finally, we train a Bayes classifier to perform action recognition. Unlike traditional approaches using the nearest neighbor rule, our proposed scheme can classify the human actions more efficiently while maintaining high accuracy. The experiment results show that the proposed scheme is efficient and accurate in human action recognition.

Original languageEnglish
Title of host publication2011 IEEE Visual Communications and Image Processing, VCIP 2011
DOIs
StatePublished - 2011
Event2011 IEEE Visual Communications and Image Processing, VCIP 2011 - Tainan, Taiwan
Duration: 6 Nov 20119 Nov 2011

Publication series

Name2011 IEEE Visual Communications and Image Processing, VCIP 2011

Conference

Conference2011 IEEE Visual Communications and Image Processing, VCIP 2011
Country/TerritoryTaiwan
CityTainan
Period6/11/119/11/11

Fingerprint

Dive into the research topics of 'Recognizing human actions using curvature estimation and NWFE-based histogram vectors'. Together they form a unique fingerprint.

Cite this