Impact of Three-Dimensional Video Scalability on Multi-View Activity Recognition using Deep Learning-Reference-Cited by-同舟云学术

Impact of Three-Dimensional Video Scalability on Multi-View Activity Recognition using Deep Learning

Published:2017 Issue: Volume: Page:
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Container-title:Proceedings of the on Thematic Workshops of ACM Multimedia 2017 - Thematic Workshops '17
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Author:

Choi Jun-Ho¹,Choi Manri¹,Choi Min-Su¹,Lee Jong-Seok¹

Affiliation:

1. Yonsei University, Incheon, South Korea

Funder

Ministry of Science, ICT and Future Planning

Publisher

ACM Press

Reference23 articles.

1. Akin Avci, Stephan Bosch, Mihai Marin-Perianu, Raluca Marin-Perianu, and Paul Havinga. 2010. Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey. In Proceedings of the Conference on Architecture of Computing Systems (ARCS). 1--10.

2. B. J. Boom, G. M. Beumer, Luuk J. Spreeuwers, and Raymond N. J. Veldhuis. 2006. The effect of image resolution on the performance of a face recognition system Proceedings of the IEEE Conference on Control, Automation, Robotics and Vision (ICARCV). 1--6.

3. Chih-Chung Chang and Chih-Jen Lin. 2011. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, Vol. 2, 3 (2011), 27.

4. Manri Cheon and Jong-Seok Lee. 2016. Evaluation of objective quality metrics for multidimensional video scalability. Journal of Visual Communication and Image Representation Vol. 35 (2016), 132--145.

5. Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, and Trevor Darrell. 2015. Long-term recurrent convolutional networks for visual recognition and description Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2625--2634.

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