Human Activity Recognition Process Using 3-D Posture Data

TLDR

The paper proposes a method for recognizing human activities from RGB‑D data captured by a Microsoft Kinect. The approach estimates key body joints from Kinect data and applies a hybrid of K‑means clustering, support vector machines, and hidden Markov models to detect, classify, and model activities as spatiotemporal sequences of postures, evaluated on the Kinect Activity Recognition Dataset, a new dataset, and CAD‑60. Experimental results show the method surpasses four prior RGB‑D approaches, achieving 77.3 % precision and 76.7 % recall while recognizing activities in real time, indicating strong practical potential.

Abstract

In this paper, we present a method for recognizing human activities using information sensed by an RGB-D camera, namely the Microsoft Kinect. Our approach is based on the estimation of some relevant joints of the human body by means of the Kinect; three different machine learning techniques, i.e., K-means clustering, support vector machines, and hidden Markov models, are combined to detect the postures involved while performing an activity, to classify them, and to model each activity as a spatiotemporal evolution of known postures. Experiments were performed on Kinect Activity Recognition Dataset, a new dataset, and on CAD-60, a public dataset. Experimental results show that our solution outperforms four relevant works based on RGB-D image fusion, hierarchical Maximum Entropy Markov Model, Markov Random Fields, and Eigenjoints, respectively. The performance we achieved, i.e., precision/recall of 77.3% and 76.7%, and the ability to recognize the activities in real time show promise for applied use.