• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Human Activity Recognition Using Spatiotemporal 3-D Body Joint Features with Hidden Markov Models


Abstract

Video-based human-activity recognition has become increasingly popular due to the prominent corresponding applications in a variety of fields such as computer vision, image processing, smart-home healthcare, and human–computer interactions. The essential goals of a video-based activity-recognition system include the provision of behavior-based information to enable functionality that proactively assists a person with his/her tasks. The target of this work is the development of a novel approach for human-activity recognition, whereby human-body-joint features that are extracted from depth videos are used. From silhouette images taken at every depth, the direction and magnitude features are first obtained from each connected body-joint pair so that they can be augmented later with motion direction, as well as with the magnitude features of each joint in the next frame. A generalized discriminant analysis (GDA) is applied to make the spatiotemporal features more robust, followed by the feeding of the time-sequence features into a Hidden Markov Model (HMM) for the training of each activity. Lastly, all of the trained-activity HMMs are used for depth-video activity recognition.


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Cite this article

[IEEE Style]
Md. Zia Uddin and Jaehyoun Kim, "Human Activity Recognition Using Spatiotemporal 3-D Body Joint Features with Hidden Markov Models," KSII Transactions on Internet and Information Systems, vol. 10, no. 6, pp. 2767-2780, 2016. DOI: 10.3837/tiis.2016.06.017

[ACM Style]
Uddin, M. Z. and Kim, J. 2016. Human Activity Recognition Using Spatiotemporal 3-D Body Joint Features with Hidden Markov Models. KSII Transactions on Internet and Information Systems, 10, 6, (2016), 2767-2780. DOI: 10.3837/tiis.2016.06.017