Performance Evaluation of Visual Tracking Algorithms on Video Sequences With Quality Degradation

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 degradat...

Full description

Saved in:
Bibliographic Details
Main Authors: Fang, Yuming, Yuan, Yuan, Li, Leida, Wu, Jinjian, Lin, Weisi, Li, Zhiqiang
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2018
Subjects:
Online Access:https://hdl.handle.net/10356/86821
http://hdl.handle.net/10220/44259
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-86821
record_format dspace
spelling sg-ntu-dr.10356-868212020-03-07T11:48:58Z Performance Evaluation of Visual Tracking Algorithms on Video Sequences With Quality Degradation Fang, Yuming Yuan, Yuan Li, Leida Wu, Jinjian Lin, Weisi Li, Zhiqiang School of Computer Science and Engineering Quality Degradation Performance Evaluation 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. Published version 2018-01-08T05:16:31Z 2019-12-06T16:29:37Z 2018-01-08T05:16:31Z 2019-12-06T16:29:37Z 2017 Journal Article 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. https://hdl.handle.net/10356/86821 http://hdl.handle.net/10220/44259 10.1109/ACCESS.2017.2666218 en IEEE Access © 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. 12 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Quality Degradation
Performance Evaluation
spellingShingle Quality Degradation
Performance Evaluation
Fang, Yuming
Yuan, Yuan
Li, Leida
Wu, Jinjian
Lin, Weisi
Li, Zhiqiang
Performance Evaluation of Visual Tracking Algorithms on Video Sequences With Quality Degradation
description 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.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Fang, Yuming
Yuan, Yuan
Li, Leida
Wu, Jinjian
Lin, Weisi
Li, Zhiqiang
format Article
author Fang, Yuming
Yuan, Yuan
Li, Leida
Wu, Jinjian
Lin, Weisi
Li, Zhiqiang
author_sort Fang, Yuming
title Performance Evaluation of Visual Tracking Algorithms on Video Sequences With Quality Degradation
title_short Performance Evaluation of Visual Tracking Algorithms on Video Sequences With Quality Degradation
title_full Performance Evaluation of Visual Tracking Algorithms on Video Sequences With Quality Degradation
title_fullStr Performance Evaluation of Visual Tracking Algorithms on Video Sequences With Quality Degradation
title_full_unstemmed Performance Evaluation of Visual Tracking Algorithms on Video Sequences With Quality Degradation
title_sort performance evaluation of visual tracking algorithms on video sequences with quality degradation
publishDate 2018
url https://hdl.handle.net/10356/86821
http://hdl.handle.net/10220/44259
_version_ 1681046856013971456