MSSTResNet-TLD: A robust tracking method based on tracking-learning-detection framework by using multi-scale spatio-temporal residual network feature model-Reference-Cited by-同舟云学术

MSSTResNet-TLD: A robust tracking method based on tracking-learning-detection framework by using multi-scale spatio-temporal residual network feature model

Published:2019-10 Issue: Volume:362 Page:175-194
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Liu Bing,Liu Qiao,Zhang Taiping,Yang Yong

Funder

National Natural Science Foundations of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference84 articles.

1. A review of visual tracking;Cannons;Dept. Comput. Sci. Eng., York Univ., Toronto, Canada, Tech. Rep. CSE-2008-07,2008

3. Recent advances and trends in visual tracking: a review;Yang;Neurocomputing,2011

4. Deep visual tracking: review and experimental comparison;Li;Pattern Recognit.,2018

5. Visually interpretable representation learning for depression recognition from facial images;Zhou;IEEE Trans. Affect. Comput.,2018

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