Profile HMMs for skeleton-based human action recognition

Abstract

In this paper, a novel approach based Hidden Markov Models (HMMs) approach is proposed for human action recognition using 3D positions of body joints. Unlike existing works, this paper addresses the challenging problem of spatio-temporal alignment of human actions which come from intra-class variability and inter-class similarity of actions. The first and foremost actions are segmented into meaningful action-units called dynamic instants and intervals by using motion velocities, the direction of motion, and the curvatures of 3D trajectories. Then action-units with its spatio-temporal feature sets are clustered using unsupervised learning, like Self-Organizing Mapping (SOM), to generate a sequence of discrete symbols. To overcome an abrupt change or an abnormal in its gesticulation between different appearances of the same kind of action, profile HMMs are applied with these symbol sequences using Viterbi and Baum–Welch algorithms for human activity recognition. The effectiveness of the proposed method is evaluated on three challenging 3D action datasets captured by commodity depth cameras. The experimental evaluations show that the proposed approach achieves promising results compared to other state-of-the-art algorithms.