Vote Distribution Model for Hough-Based Action Detection
Author:
Affiliation:
1. Graduate School of Information Science, Nagoya University
2. Graduate Program for Real-World Data Circulation Leaders, Nagoya University
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Subject
Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software
Link
https://www.jstage.jst.go.jp/article/transinf/E99.D/11/E99.D_2015EDP7503/_pdf
Reference34 articles.
1. [1] H. Wang, A. Kläser, C. Schmid, and C.-L. Liu, “Dense trajectories and motion boundary descriptors for action recognition,” International Journal of Computer Vision, vol.103, no.1, pp.60-79, 2013.
2. [2] Y. Ke, R. Sukthankar, and M. Hebert, “Event detection in crowded videos,” Proc. International Conference on Computer Vision, pp.1-8, 2007.
3. [3] L. Cao, Z. Liu, and T.S. Huang, “Cross-dataset action detection,” Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.1998-2005, 2010.
4. [4] K.G. Derpanis, M. Sizintsev, K. Cannons, and R.P. Wildes, “Efficient action spotting based on a spacetime oriented structure representation,” Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.1990-1997, 2010.
5. [5] P. Siva and T. Xiang, “Action detection in crowd,” Proc. British Machine Vision Conference, pp.9.1-9.11, 2010.
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