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Human Activity Recognition Using Combinatorial Deep Belief Networks

Gowda, Shreyank N (2017) Human Activity Recognition Using Combinatorial Deep Belief Networks. In: 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017, 21 - 26 July 2017, Honolulu, pp. 1589-1594.

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Official URL: https://doi.org/10.1109/CVPRW.2017.203


Human activity recognition is a topic undergoing a great amount of research. The main reason for that is the number of practical applications that are developed using activity recognition as the base. This paper proposes an approach to human activity recognition using a combination of deep belief networks. One network is used to obtain features from motion and to do this we propose a modified Weber descriptor. Another network is used to obtain features from images and to do this we propose the modification of the standard local binary patterns descriptor to obtain a concatenated histogram of lower dimensions. This helps to encode spatial and temporal information of various actions happening in a frame. This further helps to overcome the dimensionality problem that occurs with LBP. The features extracted are then passed onto a CNN that classifies the activity. Few standard activities are considered such as walking, sprinting, hugging etc. Results showed that the proposed algorithm gave a high level of accuracy for classification.

Item Type: Conference Paper
Publisher: IEEE Computer Society
Additional Information: The copyright for this article belongs to the IEEE Computer Society.
Department/Centre: Others
Date Deposited: 09 Jun 2022 05:09
Last Modified: 09 Jun 2022 05:09
URI: https://eprints.iisc.ac.in/id/eprint/73195

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