An Improved Real-Time Object Tracking Algorithm Based on Deep Learning Features-Reference-Cited by-同舟云学术

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An Improved Real-Time Object Tracking Algorithm Based on Deep Learning Features

Published:2023-05-01 Issue:5 Volume:E106.D Page:786-793
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

WANG Xianyu¹,LI Cong²,LI Heyi³,ZHANG Rui⁴,LIANG Zhifeng⁴,WANG Hai³

Affiliation:

1. State Key Laboratory of Integrated Service Networks, Xidian University

2. Academy of Space Electronic Information Technology

3. School of Aerospace Science and Technology, Xidian University

4. Shaanxi Aerospace Technology Application Research Institute Co., Ltd.

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/E106.D/5/E106.D_2022DLP0039/_pdf

Reference40 articles.

1. [1] N. Wang, J. Shi, D.-Y. Yeung, and J. Jia, “Understanding and diagnosing visual tracking systems,” In Proc. IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 7-13 Dec. 2015, pp.3101-3109, 2015. 10.1109/iccv.2015.355

2. [2] N. Zeng, H. Li, and Y. Peng, “A new deep belief network-based multi-task learning for diagnosis of Alzheimer's disease,” Neural Computing and Applications, 2021. https://doi.org/10.1007/s00521-021-06149-6 10.1007/s00521-021-06149-6

3. [3] Y. Wu, J. Lim, and M.-H. Yang, “Online object tracking: A benchmark,” IEEE Conference on Computer Vision and Pattern Recognition, pp.2411-2418, 2013. 10.1109/cvpr.2013.312

4. [4] Y. Wu, J. Lim, and M.-H. Yang, “Object Tracking Benchmark,” IEEE Trans. Pattern Anal. Mach. Intell., vol.37, no.9, pp.1834-1848, 2015. 10.1109/tpami.2014.2388226

5. [5] M. Kristan, J. Matas, A. Leonardis, T. Vojíř, R. Pflugfelder, G. Fernández, G. Nebehay, F. Porikli, and L. Čehovin “A novel performance evaluation methodology for single-target trackers,” IEEE Trans. Pattern Anal. Mach. Intell., vol.38, no.11, pp.2137-2155, 2016. 10.1109/tpami.2016.2516982

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