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Human Activity Recognition in Images Using SVMs and Geodesics on Smooth Manifolds

Yixiao Yun (Institutionen för signaler och system, Signalbehandling) ; Keren Fu (Institutionen för signaler och system, Signalbehandling) ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling) ; Hamid Aghajan ; Jie Yang
8th ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2014; Venezia; Italy; 4 November 2014 through 7 November 2014 p. Art. no. a20. (2014)
[Konferensbidrag, refereegranskat]

This paper addresses the problem of human activity recognition in still images. We propose a novel method that focuses on human-object interaction for feature representation of activities on Riemannian manifolds, and exploits underlying Riemannian geometry for classification. The main contributions of the paper include: (a) represent human activity by appearance features from local patches centered at hands containing interacting objects, and by structural features formed from the detected human skeleton containing the head, torso axis and hands; (b) formulate SVM kernel function based on geodesics on Riemannian manifolds under the log-Euclidean metric; (c) apply multi-class SVM classifier on the manifold under the one-against-all strategy. Experiments were conducted on a dataset containing 17196 images in 12 classes of activities from 4 subjects. Test results, evaluations, and comparisons with state-of-the-art methods provide support to the effectiveness of the proposed scheme.

Nyckelord: Human activity recognition, Riemannian manifold, covariance descriptor, symmetric positive definite (SPD) matrices, support vector machines (SVMs)



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Denna post skapades 2014-08-23. Senast ändrad 2014-12-22.
CPL Pubid: 201862

 

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