3D Tracker-Level Fusion for Robust RGB-D Tracking-Reference-Cited by-同舟云学术

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3D Tracker-Level Fusion for Robust RGB-D Tracking

Published:2017 Issue:8 Volume:E100.D Page:1870-1881
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

AN Ning¹²,ZHAO Xiao-Guang¹²,HOU Zeng-Guang¹²

Affiliation:

1. Institute of Automation, Chinese Academy of Sciences

2. University of Chinese Academy of Sciences

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/E100.D/8/E100.D_2016EDP7498/_pdf

Reference52 articles.

1. [1] A. Yilmaz, O. Javed, and M. Shah, “Object tracking: A survey,” Acm computing surveys, vol.38, no.4, p.13, 2006. 10.1145/1177352.1177355

2. [2] A.W. Smeulders, D.M. Chu, R. Cucchiara, S. Calderara, A. Dehghan, and M. Shah, “Visual tracking: An experimental survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, no.7, pp.1442-1468, 2014. 10.1109/tpami.2013.230

3. [3] Y. Wu, J. Lim, and M.-H. Yang, “Object tracking benchmark,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.37, no.9, pp.1834-1848, 2015. 10.1109/tpami.2014.2388226

4. [4] J. Han, L. Shao, D. Xu, and J. Shotton, “Enhanced computer vision with microsoft kinect sensor: A review,” IEEE Transactions on Cybernetics, vol.43, no.5, pp.1318-1334, 2013. 10.1109/tcyb.2013.2265378

5. [5] G.M. García, D.A. Klein, J. Stückler, S. Frintrop, and A.B. Cremers, “Adaptive multi-cue 3d tracking of arbitrary objects,” Pattern Recognition, vol.7476, pp.357-366, 2012. 10.1007/978-3-642-32717-9_36

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