Robust tracking based on H-CNN with low-resource sampling and scaling by frame-wise motion localization-Reference-Cited by-同舟云学术

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Robust tracking based on H-CNN with low-resource sampling and scaling by frame-wise motion localization

Published:2017-02-22 Issue:14 Volume:77 Page:18781-18800
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Zhang Peng,Zhuo Tao,Huang Hanqiao,Chen Kangli,Zhang Bo,Kankanhalli Mohan

Funder

National Natural Science Foundation of China

National Research Foundation, Prime Ministerâ€™s Office, Singapore under its International Research Centre in Singapore Funding Initiative

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

http://link.springer.com/article/10.1007/s11042-017-4493-4/fulltext.html

Reference36 articles.

1. Babenko B, Yang M.-H., Belongie S (2009) Visual tracking with online multiple instance learning. In: IEEE International conference on computer vision and pattern recognition (CVPR)

2. Cai Z, Gu Z, Y ZL, Liu H (2016) A real-time visual object tracking system based on kalman filter and mb-lbp feature matching. Multimed Tool Appl (MTAP) 75:2393–2409

3. Choi JW, Whangbo TK, Kim CG (2015) A contour tracking method of large motion object using optical flow and active contour model. Multimed Tool Appl (MTAP) 74:199–210

4. Danelljan M, Hager G, Khan F, Felsberg M (2014) Accurate scale estimation for robust visual tracking. In: British machine vision conference (BMVC), pp 1–11

5. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: IEEE International conference on computer vision and pattern recognition (CVPR)

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