Silhouette Analysis-Based Action Recognition Via Exploiting Human Poses

Abstract

In this paper, we propose a novel scheme for human action recognition that combines the advantages of both local and global representations. We explore human silhouettes for human action representation by taking into account the correlation between sequential poses in an action. A modified bag-of-words model, named bag of correlated poses, is introduced to encode temporally local features of actions. To utilize the property of visual word ambiguity, we adopt the soft assignment strategy to reduce the dimensionality of our model and circumvent the penalty of computational complexity and quantization error. To compensate for the loss of structural information, we propose an extended motion template, i.e., extensions of the motion history image, to capture the holistic structural features. The proposed scheme takes advantages of local and global features and, therefore, provides a discriminative representation for human actions. Experimental results prove the viability of the complimentary properties of two descriptors and the proposed approach outperforms the state-of-the-art methods on the IXMAS action recognition dataset.