Recognizing Fine-Grained and Composite Activities Using Hand-Centric Features and Script Data-Reference-Cited by-同舟云学术

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Recognizing Fine-Grained and Composite Activities Using Hand-Centric Features and Script Data

Published:2015-08-22 Issue:3 Volume:119 Page:346-373
ISSN:0920-5691
Container-title:International Journal of Computer Vision
language:en
Short-container-title:Int J Comput Vis

Author:

Rohrbach Marcus,Rohrbach Anna,Regneri Michaela,Amin Sikandar,Andriluka Mykhaylo,Pinkal Manfred,Schiele Bernt

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

http://link.springer.com/content/pdf/10.1007/s11263-015-0851-8.pdf

Reference116 articles.

1. Amin, S., Andriluka, M., Rohrbach, M., & Schiele, B. (2013). Multi-view pictorial structures for 3D human pose estimation. In Proceedings of the British Machine Vision Conference (BMVC). BMVA Press.

2. Andriluka, M., Roth, S., & Schiele, B. (2009). Pictorial structures revisited: People detection and articulated pose estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

3. Andriluka, M., Roth, S., & Schiele, B. (2011). Discriminative appearance models for pictorial structures. International Journal of Computer Vision (IJCV), 99, 259–280.

4. Aubert, O., & Prié, Y. (2007). Advene: An open-source framework for integrating and visualising audiovisual metadata. In MM. ACM.

5. Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., & Baskurt, A. (2011). Sequential deep learning for human action recognition. In Human Behavior Understanding (pp. 29–39). Springer.

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