Object Tracking Benchmark

Object tracking has been one of the most important and active research areas in the field of computer vision. A large number of tracking algorithms have been proposed in recent years with demonstrated success. However, the set of sequences used for evaluation is often not sufficient or is sometimes...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 37; H. 9; S. 1834 - 1848
Hauptverfasser:	Wu, Yi, Lim, Jongwoo, Yang, Ming-Hsuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.09.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithm design and analysis Algorithms benchmark dataset Histograms Object tracking Performance evaluation Robustness Target tracking performance evaluation Object tracking benchmark dataset
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:	Volltext
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Abstract	Object tracking has been one of the most important and active research areas in the field of computer vision. A large number of tracking algorithms have been proposed in recent years with demonstrated success. However, the set of sequences used for evaluation is often not sufficient or is sometimes biased for certain types of algorithms. Many datasets do not have common ground-truth object positions or extents, and this makes comparisons among the reported quantitative results difficult. In addition, the initial conditions or parameters of the evaluated tracking algorithms are not the same, and thus, the quantitative results reported in literature are incomparable or sometimes contradictory. To address these issues, we carry out an extensive evaluation of the state-of-the-art online object-tracking algorithms with various evaluation criteria to understand how these methods perform within the same framework. In this work, we first construct a large dataset with ground-truth object positions and extents for tracking and introduce the sequence attributes for the performance analysis. Second, we integrate most of the publicly available trackers into one code library with uniform input and output formats to facilitate large-scale performance evaluation. Third, we extensively evaluate the performance of 31 algorithms on 100 sequences with different initialization settings. By analyzing the quantitative results, we identify effective approaches for robust tracking and provide potential future research directions in this field.
AbstractList	Object tracking has been one of the most important and active research areas in the field of computer vision. A large number of tracking algorithms have been proposed in recent years with demonstrated success. However, the set of sequences used for evaluation is often not sufficient or is sometimes biased for certain types of algorithms. Many datasets do not have common ground-truth object positions or extents, and this makes comparisons among the reported quantitative results difficult. In addition, the initial conditions or parameters of the evaluated tracking algorithms are not the same, and thus, the quantitative results reported in literature are incomparable or sometimes contradictory. To address these issues, we carry out an extensive evaluation of the state-of-the-art online object-tracking algorithms with various evaluation criteria to understand how these methods perform within the same framework. In this work, we first construct a large dataset with ground-truth object positions and extents for tracking and introduce the sequence attributes for the performance analysis. Second, we integrate most of the publicly available trackers into one code library with uniform input and output formats to facilitate large-scale performance evaluation. Third, we extensively evaluate the performance of 31 algorithms on 100 sequences with different initialization settings. By analyzing the quantitative results, we identify effective approaches for robust tracking and provide potential future research directions in this field. Object tracking has been one of the most important and active research areas in the field of computer vision. A large number of tracking algorithms have been proposed in recent years with demonstrated success. However, the set of sequences used for evaluation is often not sufficient or is sometimes biased for certain types of algorithms. Many datasets do not have common ground-truth object positions or extents, and this makes comparisons among the reported quantitative results difficult. In addition, the initial conditions or parameters of the evaluated tracking algorithms are not the same, and thus, the quantitative results reported in literature are incomparable or sometimes contradictory. To address these issues, we carry out an extensive evaluation of the state-of-the-art online object-tracking algorithms with various evaluation criteria to understand how these methods perform within the same framework. In this work, we first construct a large dataset with ground-truth object positions and extents for tracking and introduce the sequence attributes for the performance analysis. Second, we integrate most of the publicly available trackers into one code library with uniform input and output formats to facilitate large-scale performance evaluation. Third, we extensively evaluate the performance of 31 algorithms on 100 sequences with different initialization settings. By analyzing the quantitative results, we identify effective approaches for robust tracking and provide potential future research directions in this field.Object tracking has been one of the most important and active research areas in the field of computer vision. A large number of tracking algorithms have been proposed in recent years with demonstrated success. However, the set of sequences used for evaluation is often not sufficient or is sometimes biased for certain types of algorithms. Many datasets do not have common ground-truth object positions or extents, and this makes comparisons among the reported quantitative results difficult. In addition, the initial conditions or parameters of the evaluated tracking algorithms are not the same, and thus, the quantitative results reported in literature are incomparable or sometimes contradictory. To address these issues, we carry out an extensive evaluation of the state-of-the-art online object-tracking algorithms with various evaluation criteria to understand how these methods perform within the same framework. In this work, we first construct a large dataset with ground-truth object positions and extents for tracking and introduce the sequence attributes for the performance analysis. Second, we integrate most of the publicly available trackers into one code library with uniform input and output formats to facilitate large-scale performance evaluation. Third, we extensively evaluate the performance of 31 algorithms on 100 sequences with different initialization settings. By analyzing the quantitative results, we identify effective approaches for robust tracking and provide potential future research directions in this field.
Author	Wu, Yi Yang, Ming-Hsuan Lim, Jongwoo
Author_xml	– sequence: 1 givenname: Yi orcidid: 0000-0003-4848-2304 surname: Wu fullname: Wu, Yi email: ywu.china@gmail.com organization: Department of Computer Science, Nanjing University of Information Science and Technology, Nanjing, Nanjing, China – sequence: 2 givenname: Jongwoo surname: Lim fullname: Lim, Jongwoo email: jlim@hanyang.ac.kr organization: Department of Computer Science and Engineering, Hanyang University, Seoul, Seoul, Republic of Korea – sequence: 3 givenname: Ming-Hsuan surname: Yang fullname: Yang, Ming-Hsuan email: mhyang@ucmerced.edu organization: Department of Electrical Engineering and Computer Science, University of California at Merced, Merced, CA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/26353130$$D View this record in MEDLINE/PubMed
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Snippet	Object tracking has been one of the most important and active research areas in the field of computer vision. A large number of tracking algorithms have been...
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StartPage	1834
SubjectTerms	Algorithm design and analysis Algorithms benchmark dataset Histograms Object tracking Performance evaluation Robustness Target tracking
Title	Object Tracking Benchmark
URI	https://ieeexplore.ieee.org/document/7001050 https://www.ncbi.nlm.nih.gov/pubmed/26353130 https://www.proquest.com/docview/1728034276 https://www.proquest.com/docview/1711535957
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