Riemannian Manifold-Based Support Vector Machine for Human Activity Classification in Images
Paper i proceeding, 2013

This paper addresses the issue of classification of human activities in still images. We propose a novel method where part-based features focusing on human and object interaction are utilized for activity representation, and classification is designed on manifolds by exploiting underlying Riemannian geometry. The main contributions of the paper include: (a) represent human activity by appearance features from image patches containing hands, and by structural features formed from the distances between the torso and patch centers; (b) formulate SVM kernel function based on the 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 2750 images in 7 classes of activities from 10 subjects. Results have shown good performance (average classification rate of 95.83%, false positive 0.71%, false negative 4.24%). Comparisons with three other related classifiers provide further support to the proposed method.

Human activity classification

symmetric positive definite matrices

Riemannian manifold

support vector machines.

covariance descriptor

Författare

Yixiao Yun

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Irene Yu-Hua Gu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Hamid Aghajan

Stanford University

IEEE International Conference on Image Processing (ICIP 2013), Sept.15 - 18, Melbourne, Australia

3466-3469

Styrkeområden

Informations- och kommunikationsteknik

Transport

Ämneskategorier

Geometri

Systemvetenskap

Signalbehandling

Datorseende och robotik (autonoma system)

DOI

10.1109/ICIP.2013.6738715

ISBN

9781479923410