Abstract
The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending the evaluation system with the no-reset experiment. The dataset, the evaluation kit as well as the results are publicly available at the challenge website (http: //votchallenge.net).
| Original language | English |
|---|---|
| Title of host publication | Computer Vision – ECCV 2016 Workshops, Proceedings |
| Editors | Gang Hua, Herve Jegou |
| Publisher | Springer Verlag |
| Pages | 777-823 |
| Number of pages | 47 |
| ISBN (Print) | 9783319488806 |
| DOIs | |
| Publication status | Published - 2016 |
| Event | Computer Vision - ECCV 2016 Workshops, Proceedings - Amsterdam, Netherlands Duration: 8 Oct 2016 → 16 Oct 2016 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 9914 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | Computer Vision - ECCV 2016 Workshops, Proceedings |
|---|---|
| Country/Territory | Netherlands |
| City | Amsterdam |
| Period | 8/10/16 → 16/10/16 |
Bibliographical note
Publisher Copyright:© Springer International Publishing Switzerland 2016.
Keywords
- Performance evaluation
- Short-term single-object trackers
- VOT
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