Two-Stream RNN/CNN For Action Recognition In 3D Videos - ArXiv

Computer Science > Computer Vision and Pattern Recognition arXiv:1703.09783 (cs) [Submitted on 22 Mar 2017 (v1), last revised 2 Oct 2018 (this version, v2)] Title:Two-Stream RNN/CNN for Action Recognition in 3D Videos Authors:Rui Zhao, Haider Ali, Patrick van der Smagt View a PDF of the paper titled Two-Stream RNN/CNN for Action Recognition in 3D Videos, by Rui Zhao and 2 other authors View PDF

Abstract:The recognition of actions from video sequences has many applications in health monitoring, assisted living, surveillance, and smart homes. Despite advances in sensing, in particular related to 3D video, the methodologies to process the data are still subject to research. We demonstrate superior results by a system which combines recurrent neural networks with convolutional neural networks in a voting approach. The gated-recurrent-unit-based neural networks are particularly well-suited to distinguish actions based on long-term information from optical tracking data; the 3D-CNNs focus more on detailed, recent information from video data. The resulting features are merged in an SVM which then classifies the movement. In this architecture, our method improves recognition rates of state-of-the-art methods by 14% on standard data sets.

Comments:	Published in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Subjects:	Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as:	arXiv:1703.09783 [cs.CV]
(or arXiv:1703.09783v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.1703.09783 Focus to learn more arXiv-issued DOI via DataCite
Related DOI:	https://doi.org/10.1109/IROS.2017.8206288 Focus to learn more DOI(s) linking to related resources

Submission history

From: Rui Zhao [view email] [v1] Wed, 22 Mar 2017 22:29:56 UTC (4,926 KB) [v2] Tue, 2 Oct 2018 16:16:31 UTC (4,978 KB) Full-text links:

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