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Krishnan, Kumaresh and Prabhu, Nikita and Babu, Venkatesh R (2016) ARRNET: ACTION RECOGNITION THROUGH RECURRENT NEURAL NETWORKS. In: 11th International Conference on Signal Processing and Communications (SPCOM), JUN 12-15, 2016, Indian Inst Sci, Banglore, INDIA.

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Official URL: http://dx.doi.org/10.1109/SPCOM.2016.7746614


In this paper, we propose a novel method for recognition of human actions from joint points. Our approach utilizes Long Short Term Memory (LSTM), a Recurrent Neural Network (RNN) variant to keep track of and train the network on joint information across an ordered sample of 15 frames from a video. We ensure that important properties of actions like left right invariance are learnt by the system through data augmentation. Our experiments on sub-JHMDB and Penn Action datasets provide encouraging results which surpass previous action recognition models on these datasets. We analyse our model on the results obtained for tests on these datasets.

Item Type: Conference Proceedings
Additional Information: Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre: Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited: 31 Jan 2017 05:32
Last Modified: 27 Nov 2018 15:45
URI: http://eprints.iisc.ac.in/id/eprint/56150

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