Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/86821
Title: Performance Evaluation of Visual Tracking Algorithms on Video Sequences With Quality Degradation
Authors: Fang, Yuming
Yuan, Yuan
Li, Leida
Wu, Jinjian
Lin, Weisi
Li, Zhiqiang
Keywords: Quality Degradation
Performance Evaluation
Issue Date: 2017
Source: Fang, Y., Yuan, Y., Li, L., Wu, J., Lin, W., & Li, Z. (2017). Performance Evaluation of Visual Tracking Algorithms on Video Sequences With Quality Degradation. IEEE Access, 5, 2430-2441.
Series/Report no.: IEEE Access
Abstract: Recently, there are lots of visual tracking algorithms proposed to improve the performance of object tracking in video sequences with various real conditions, such as severe occlusion, complicated background, fast motion, and so on. In real visual tracking systems, there are various quality degradation occurring during video acquisition, transmission, and processing. However, most existing studies focus on improving the accuracy of visual tracking while ignoring the performance of tracking algorithms on video sequences with certain quality degradation. In this paper, we investigate the performance evaluation of existing visual tracking algorithms on video sequences with quality degradation. A quality-degraded video database for visual tracking (QDVD-VT), including the reference video sequences and their corresponding distorted versions, is constructed as the benchmarking for robustness analysis of visual tracking algorithms. Based on the constructed QDVD-VT, we propose a method for robustness measurement of visual tracking (RMVT) algorithms by accuracy rate and performance stability. The performance of ten existing visual tracking algorithms is evaluated by the proposed RMVT based on the built QDVD-VT. We provide the detailed analysis and discussion on the robustness analysis of different visual tracking algorithms on video sequences with quality degradation from different distortion types. To visualize the robustness of visual tracking algorithms well, we design a robustness pentagon to show the accuracy rate and performance stability of visual tracking algorithms. Our initial investigation shows that it is still challenging for effective object tracking for existing visual tracking algorithms on video sequences with quality degradation. There is much room for the performance improvement of existing tracking algorithms on video sequences with quality degradation in real applications.
URI: https://hdl.handle.net/10356/86821
http://hdl.handle.net/10220/44259
DOI: http://dx.doi.org/10.1109/ACCESS.2017.2666218
Rights: © 2017 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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