Human Activity Recognition with Posture Tendency Descriptors on Action Snippets

Human activity recognition is a challenging problem in computer vision due to large resemblance across classes and variance within an individual class. A routine way to recognize human activity from 3D skeleton sequences can be divided into two tasks, discriminative features representation and temporal dynamics modeling. During the past few years, temporal pyramid is widely used for capturing temporal dynamics after extracting discriminative features from frames. However, this uninformative dividing method could destroy the geometric structure of meaningful action snippets within skeleton sequence. To resolve this problem efficiently, we propose a novel and intuitive method in this paper. First, based on a more realistic assumption that adjacent postures in action sequences are more similar and activity can be depicted with several action snippets, a dividing algorithm is designed to encode the temporal information. Second, an interpretable and discriminative descriptor named posture tendency descriptor (PTD) is constructed to represent one action snippet. Finally, multiple PTDs along the entire skeleton sequence are concatenated in a hierarchical and temporal order forming the representation of a human activity. Experimental results on three benchmark datasets demonstrate that the proposed approach with an off-the-shelf classification algorithm achieves highly competitive performance in comparison with the state-of-the-art approaches.