Position-Based Action Recognition Using High Dimension Index Tree

Most current approaches in action recognition face difficulties that cannot handle recognition of multiple actions, fusion of multiple features, and recognition of action in frame by frame model, incremental learning of new action samples and application of position information of space-time interest points to improve performance simultaneously. In this paper, we propose a novel approach based on Position-Tree that takes advantage of the relationship of the position of joints and interest points. The normalized position of interest points indicates where the movement of body part has occurred. The extraction of local feature encodes the shape of the body part when performing action, justifying body movements. Additionally, we propose a new local descriptor calculating the local energy map from spatial-temporal cuboids around interest point. In our method, there are three steps to recognize an action: (1) extract the skeleton point and space-time interest point, calculating the normalized position according to their relationships with joint position; (2) extract the LEM (Local Energy Map) descriptor around interest point; (3) recognize these local features through non-parametric nearest neighbor and label an action by voting those local features. The proposed approach is tested on publicly available MSRAction3D dataset, demonstrating the advantages and the state-of-art performance of the proposed method.