Riemannian Manifold-Based Support Vector Machine for Human Activity Classification in Images
Paper in proceedings, 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
support vector machines.