Skeleton-Based Human Action Recognition with Profile Hidden Markov Models

Recognizing human actions from image sequences is an active area of research in computer vision. In this paper, a novel HMM-based approach is proposed for human action recognition using 3D positions of body joints. First, 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 unit with its spatio-temporal feature sets are clustered using unsupervised learning, like SOM, to generate a sequence of discrete symbols. To overcome an abrupt change or an abnormal in its gesticulation between different performances of the same action, Profile Hidden Markov Models (Profile HMMs) are applied with these symbol sequences using Viterbi and Baum-Welch algorithms for human activity recognition. The experimental evaluations show that the proposed approach achieves promising results compared to other state of the art algorithms.