Conference item
The seventh visual object tracking VOT2019 Challenge results
- Abstract:
- The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOTST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" shortterm tracking in RGB, (iii) VOT-LT2019 focused on longterm tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard shortterm, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 3.0MB, Terms of use)
-
- Publisher copy:
- 10.1109/ICCVW.2019.00276
Authors
- Publisher:
- IEEE
- Pages:
- 2206-2241
- Publication date:
- 2020-03-05
- Acceptance date:
- 2019-10-27
- Event title:
- 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW 2019)
- Event website:
- https://www.computer.org/csdl/proceedings/iccvw/2019/1i5mkDyiIUg
- Event start date:
- 2019-10-27
- Event end date:
- 2019-10-28
- DOI:
- EISSN:
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2473-9944
- ISSN:
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2473-9936
- EISBN:
- 9781728150239
- ISBN:
- 9781728150246
- Language:
-
English
- Keywords:
- Pubs id:
-
1098638
- Local pid:
-
pubs:1098638
- Deposit date:
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2020-09-09
- ARK identifier:
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2019
- Rights statement:
- © 2019 IEEE.
- Notes:
- This paper was presented at the 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW 2019). This is the accepted manuscript version of the article. The final version is available online from IEEE at: https://doi.org/10.1109/ICCVW.2019.00276
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