DSRF: A flexible trajectory descriptor for articulated human action recognition

In this paper, we propose a novel skeletal representation that models the human body as the articulated interconnections of multiple rigid bodies. An articulated human action can be viewed as the combination of multiple rigid body motion trajectories. The Dual Square-Root Function (DSRF) descriptor is firstly introduced by calculating the gradient-based invariants for representing 6-D rigid body motion trajectories, which can offer substantial advantages over raw data. To effectively incorporate the DSRF descriptors in the skeletal representation, a skeleton is decomposed into five parts and the most informative part method is raised for offering compact representation. Besides, two rigid body configurations are investigated for representing the movement of each part. In the recognition stage, we first follow the simple template matching strategy with the nearest neighbor classifier. A robust distance measure between two skeletal representations is designed. In addition, the DSRF-based skeletal representation can be encoded as a sparse histogram by the bag-of-words approach. Then, a support vector machine classifier with chi-square kernel is trained for multiclass recognition tasks. Experimental results on three benchmark datasets demonstrate that our proposed approach outperforms existing skeleton representations in terms of recognition accuracy.