The visual object tracking VOT2016 challenge results
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 i...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Conference item |
| Language: | English |
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Springer
2016
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| _version_ | 1826312929072381952 |
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| author | Kristan, M Leonardis, A Matas, J Felsberg, M Pflugfelder, R Čehovin, L Vojír̃, T Häger, G Lukežič, A Fernández, G Gupta, A Petrosino, A Memarmoghadam, A Garcia-Martin, A Solís Montero, A Vedaldi, A Robinson, A Ma, AJ Varfolomieiev, A Alatan, A Erdem, A Ghanem, B Liu, B Han, B Martinez, B Chang, C-M Xu, C Sun, C Kim, D Chen, D Du, D Mishra, D Yeung, D-Y Gundogdu, E Erdem, E Khan, F Porikli, F Zhao, F Bunyak, F Battistone, F Zhu, G Roffo, G Subrahmanyam, GRKS Bastos, G Seetharaman, G Medeiros, H Li, H Qi, H Bischof, H Possegger, H Henriques, JF Torr, PHS |
| author_facet | Kristan, M Leonardis, A Matas, J Felsberg, M Pflugfelder, R Čehovin, L Vojír̃, T Häger, G Lukežič, A Fernández, G Gupta, A Petrosino, A Memarmoghadam, A Garcia-Martin, A Solís Montero, A Vedaldi, A Robinson, A Ma, AJ Varfolomieiev, A Alatan, A Erdem, A Ghanem, B Liu, B Han, B Martinez, B Chang, C-M Xu, C Sun, C Kim, D Chen, D Du, D Mishra, D Yeung, D-Y Gundogdu, E Erdem, E Khan, F Porikli, F Zhao, F Bunyak, F Battistone, F Zhu, G Roffo, G Subrahmanyam, GRKS Bastos, G Seetharaman, G Medeiros, H Li, H Qi, H Bischof, H Possegger, H Henriques, JF Torr, PHS |
| author_sort | Kristan, M |
| collection | OXFORD |
| description | 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). |
| first_indexed | 2024-09-25T04:04:33Z |
| format | Conference item |
| id | oxford-uuid:0bcfadda-c6fe-4db9-bb49-b9d21c9881e3 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:04:33Z |
| publishDate | 2016 |
| publisher | Springer |
| record_format | dspace |
| spelling | oxford-uuid:0bcfadda-c6fe-4db9-bb49-b9d21c9881e32024-05-20T13:53:50ZThe visual object tracking VOT2016 challenge resultsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:0bcfadda-c6fe-4db9-bb49-b9d21c9881e3EnglishSymplectic ElementsSpringer2016Kristan, MLeonardis, AMatas, JFelsberg, MPflugfelder, RČehovin, LVojír̃, THäger, GLukežič, AFernández, GGupta, APetrosino, AMemarmoghadam, AGarcia-Martin, ASolís Montero, AVedaldi, ARobinson, AMa, AJVarfolomieiev, AAlatan, AErdem, AGhanem, BLiu, BHan, BMartinez, BChang, C-MXu, CSun, CKim, DChen, DDu, DMishra, DYeung, D-YGundogdu, EErdem, EKhan, FPorikli, FZhao, FBunyak, FBattistone, FZhu, GRoffo, GSubrahmanyam, GRKSBastos, GSeetharaman, GMedeiros, HLi, HQi, HBischof, HPossegger, HHenriques, JFTorr, PHSThe 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). |
| spellingShingle | Kristan, M Leonardis, A Matas, J Felsberg, M Pflugfelder, R Čehovin, L Vojír̃, T Häger, G Lukežič, A Fernández, G Gupta, A Petrosino, A Memarmoghadam, A Garcia-Martin, A Solís Montero, A Vedaldi, A Robinson, A Ma, AJ Varfolomieiev, A Alatan, A Erdem, A Ghanem, B Liu, B Han, B Martinez, B Chang, C-M Xu, C Sun, C Kim, D Chen, D Du, D Mishra, D Yeung, D-Y Gundogdu, E Erdem, E Khan, F Porikli, F Zhao, F Bunyak, F Battistone, F Zhu, G Roffo, G Subrahmanyam, GRKS Bastos, G Seetharaman, G Medeiros, H Li, H Qi, H Bischof, H Possegger, H Henriques, JF Torr, PHS The visual object tracking VOT2016 challenge results |
| title | The visual object tracking VOT2016 challenge results |
| title_full | The visual object tracking VOT2016 challenge results |
| title_fullStr | The visual object tracking VOT2016 challenge results |
| title_full_unstemmed | The visual object tracking VOT2016 challenge results |
| title_short | The visual object tracking VOT2016 challenge results |
| title_sort | visual object tracking vot2016 challenge results |
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