Learning a pose lexicon for semantic action recognition

Authors

Lijuan Zhou, University of WollongongFollow
Wanqing Li, University of WollongongFollow
Philip O. Ogunbona, University of WollongongFollow

RIS ID

109644

Publication Details

Zhou, L., Li, W. & Ogunbona, P. (2016). Learning a pose lexicon for semantic action recognition. 2016 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1-6). United States: IEEE.

Abstract

This paper presents a novel method for learning a pose lexicon comprising semantic poses defined by textual instructions and their associated visual poses defined by visual features. The proposed method simultaneously takes two input streams, semantic poses and visual pose candidates, and statistically learns a mapping between them to construct the lexicon. With the learned lexicon, action recognition can be cast as the problem of finding the maximum translation probability of a sequence of semantic poses given a stream of visual pose candidates. Experiments evaluating pre-trained and zero-shot action recognition conducted on MSRC-12 gesture and WorkoutSu-10 exercise datasets were used to verify the efficacy of the proposed method.