Object tracking benchmark

Yi Wu, Jongwoo Lim, Ming Hsuan Yang

Research output: Contribution to journalArticle

1223 Citations (Scopus)

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.

Original languageEnglish
Article number7001050
Pages (from-to)1834-1848
Number of pages15
JournalIEEE transactions on pattern analysis and machine intelligence
Volume37
Issue number9
DOIs
Publication statusPublished - 2015 Sep 1

Fingerprint

Object Tracking
Benchmark
Evaluation
Initialization
Large Data Sets
Computer Vision
Computer vision
Performance Analysis
Biased
Performance Evaluation
Initial conditions
Attribute
Integrate
Sufficient
Evaluate
Output
Object
Truth

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

Cite this

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Object tracking benchmark. / Wu, Yi; Lim, Jongwoo; Yang, Ming Hsuan.

In: IEEE transactions on pattern analysis and machine intelligence, Vol. 37, No. 9, 7001050, 01.09.2015, p. 1834-1848.

Research output: Contribution to journalArticle

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