Online object tracking: A benchmark

Yi Wu, Jongwoo Lim, Ming Hsuan Yang

Research output: Contribution to journalConference articlepeer-review

3934 Citations (Scopus)


Object tracking is one of the most important components in numerous applications of computer vision. While much progress has been made in recent years with efforts on sharing code and datasets, it is of great importance to develop a library and benchmark to gauge the state of the art. After briefly reviewing recent advances of online object tracking, we carry out large scale experiments with various evaluation criteria to understand how these algorithms perform. The test image sequences are annotated with different attributes for performance evaluation and analysis. By analyzing quantitative results, we identify effective approaches for robust tracking and provide potential future research directions in this field.

Original languageEnglish
Article number6619156
Pages (from-to)2411-2418
Number of pages8
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Publication statusPublished - 2013
Event26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, United States
Duration: 2013 Jun 232013 Jun 28

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition


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