Abstract
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 language | English |
|---|---|
| Article number | 6619156 |
| Pages (from-to) | 2411-2418 |
| Number of pages | 8 |
| Journal | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
| DOIs | |
| Publication status | Published - 2013 Nov 15 |
| Event | 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, United States Duration: 2013 Jun 23 → 2013 Jun 28 |
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All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition
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Online object tracking : A benchmark. / Wu, Yi; Lim, Jongwoo; Yang, Ming Hsuan.
In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 15.11.2013, p. 2411-2418.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - Online object tracking
T2 - A benchmark
AU - Wu, Yi
AU - Lim, Jongwoo
AU - Yang, Ming Hsuan
PY - 2013/11/15
Y1 - 2013/11/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84887348427&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84887348427&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2013.312
DO - 10.1109/CVPR.2013.312
M3 - Conference article
AN - SCOPUS:84887348427
SP - 2411
EP - 2418
JO - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
JF - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SN - 1063-6919
M1 - 6619156
ER -