IEEEAcess DingMengThormal 2020.pdf (3.81 MB)
Visual tracking with online assessment and improved sampling strategy
journal contribution
posted on 2020-03-02, 12:08 authored by Meng Ding, Wen-Hua ChenWen-Hua Chen, Li Wei, Yun-Feng Cao, Zhou-Yu ZhangThe kernelized correlation filter (KCF) is one of the most successful trackers in computer vision today. However its performance may be significantly degraded in a wide range of challenging conditions such as occlusion and out of view. For many applications, particularly safety critical applications (e.g. autonomous driving), it is of profound importance to have consistent and reliable performance during all the operation conditions. This paper addresses this issue of the KCF based trackers by the introduction of two novel modules, namely online assessment of response map, and a strategy of combining cyclically shifted sampling with random sampling in deep feature space. A method of online assessment of response map is proposed to evaluate the tracking performance by constructing a 2-D Gaussian estimation model. Then a strategy of combining cyclically shifted sampling with random sampling in deep feature space is presented to improve the tracking performance when the tracking performance is assessed to be unreliable based on the response map. Therefore, the module of online assessment can be regarded as the trigger for the second module. Experiments verify the tracking performance is significantly improved particularly in challenging conditions as demonstrated by both quantitative and qualitative comparisons of the proposed tracking algorithm with the state-of-the-art tracking algorithms on OTB-2013 and OTB-2015 datasets.
Funding
National Natural Science Foundation of China under Grant 61673211, Grant U1633105, and Grant 61203170
Aeronautical Science Foundation of China under Grant 20155152041 and Grant 20170752008
Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 18KJB590002
Open Funds for Key Laboratory of Civil Aircraft Health Monitoring and Intelligent Maintenance under Grant NJ2018012
History
School
- Aeronautical, Automotive, Chemical and Materials Engineering
Department
- Aeronautical and Automotive Engineering
Published in
IEEE AccessVolume
8Pages
36948 - 36962Publisher
Institute of Electrical and Electronics Engineers (IEEE)Version
- VoR (Version of Record)
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© The AuthorsPublisher statement
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/Acceptance date
2020-02-16Publication date
2020-02-20Copyright date
2020eISSN
2169-3536Publisher version
Language
- en
Depositor
Prof Wen-Hua Chen. Deposit date: 1 March 2020Usage metrics
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