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

A Robust Approach for Human Activity Recognition Using 3-D Body Joint Motion Features with Deep Belief Network

Vol. 11, No.2, February 28, 2017
10.3837/tiis.2017.02.028, Full Text:

Abstract

Computer vision-based human activity recognition (HAR) has become very famous these days due to its applications in various fields such as smart home healthcare for elderly people. A video-based activity recognition system basically has many goals such as to react based on people’s behavior that allows the systems to proactively assist them with their tasks. A novel approach is proposed in this work for depth video based human activity recognition using joint-based motion features of depth body shapes and Deep Belief Network (DBN). From depth video, different body parts of human activities are segmented first by means of a trained random forest. The motion features representing the magnitude and direction of each joint in next frame are extracted. Finally, the features are applied for training a DBN to be used for recognition later. The proposed HAR approach showed superior performance over conventional approaches on private and public datasets, indicating a prominent approach for practical applications in smartly controlled environments.


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Cite this article

[IEEE Style]
Md. Zia Uddin and Jaehyoun Kim, "A Robust Approach for Human Activity Recognition Using 3-D Body Joint Motion Features with Deep Belief Network," KSII Transactions on Internet and Information Systems, vol. 11, no. 2, pp. 1118-1133, 2017. DOI: 10.3837/tiis.2017.02.028

[ACM Style]
Uddin, M. Z. and Kim, J. 2017. A Robust Approach for Human Activity Recognition Using 3-D Body Joint Motion Features with Deep Belief Network. KSII Transactions on Internet and Information Systems, 11, 2, (2017), 1118-1133. DOI: 10.3837/tiis.2017.02.028