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Proceedings Paper

Human action recognition based on two-stream Ind recurrent neural network
Author(s): Penghua Ge; Min Zhi
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Paper Abstract

In order to avoid the influence of external factors on the subsequent recognition of RGB video and improve the accuracy of human motion recognition, an algorithm of human action recognition based on Two-Stream Ind Recurrent Neural Network is proposed. In terms of extracting features, the temporal network extracts the information on the 3D coordinate of different joints at each time and classifies it by a softmax layer. The spatial network converts the spatial positional relationship of the joints at each moment into a skeleton sequence and inputs it into the softmax layer to classify. Finally, the results of the classification of the temporal network and the spatial network are weighted and summed to obtain the final classification result. Experiments verify the validity of the model on the largest 3D skeleton action recognition dataset NTU RGB + D and SBU interactive dataset.

Paper Details

Date Published: 6 May 2019
PDF: 7 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110693C (6 May 2019); doi: 10.1117/12.2524322
Show Author Affiliations
Penghua Ge, Inner Mongolia Normal Univ. (China)
Min Zhi, Inner Mongolia Normal Univ. (China)

Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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