Human Activity Recognition Process Using 3-D Posture Data

• Published in: IEEE transactions on human-machine systems Vol. 45; no. 5; pp. 586 - 597
• Main Authors: Gaglio, Salvatore, Re, Giuseppe Lo, Morana, Marco
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cameras; Datasets; Feature extraction; Hidden Markov models; Human activity recognition; Human motion; Human motion body; Image processing; Joints; kinect; Markov analysis; Markov models; Mathematical models; Performance evaluation; Real-time systems; Recall; Recognition; Support vector machines; Three dimensional; Vector quantization; Human activity recognition; kinect
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2014.2377111

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.