Effective Pose-based Approach with Pose Estimation for Emotional Action Recognition


KIPS Transactions on Software and Data Engineering, Vol. 2, No. 3, pp. 209-218, Mar. 2013
10.3745/KTSDE.2013.2.3.209,   PDF Download:

Abstract

Early researches in human action recognition have focused on tracking and classifying articulated body motions. Such methods required accurate segmentation of body parts, which is a sticky task, particularly under realistic imaging conditions. Recent trends of work have become popular towards the use of more abstract and low-level appearance features such as spatio-temporal interest points. Given the great progress in pose estimation over the past few years, redefined views about pose-based approach are needed. This paper addresses the issues of whether it is sufficient to train a classifier only on low-level appearance features in appearance approach and proposes effective pose-based approach with pose estimation for emotional action recognition. In order for these questions to be solved, we compare the performance of pose-based, appearance-based and its combination-based features respectively with respect to scenario of various emotional action recognition. The experiment results show that pose-based features outperform low-level appearance-based approach of features, even when heavily spoiled by noise, suggesting that pose-based approach with pose estimation is beneficial for the emotional action recognition.


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Cite this article
[IEEE Style]
J. O. Kim, "Effective Pose-based Approach with Pose Estimation for Emotional Action Recognition," KIPS Transactions on Software and Data Engineering, vol. 2, no. 3, pp. 209-218, 2013. DOI: 10.3745/KTSDE.2013.2.3.209.

[ACM Style]
Jin Ok Kim. 2013. Effective Pose-based Approach with Pose Estimation for Emotional Action Recognition. KIPS Transactions on Software and Data Engineering, 2, 3, (2013), 209-218. DOI: 10.3745/KTSDE.2013.2.3.209.