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 maj
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na, Ljubljana, Slovenia [email protected] 2 University of Birmingham, Birmingham, England 3 Czech Technical University, Praha, Czech Republic c Springer International Publishing Switzerland 2016 G. Hua and H. J´ egou (Eds.): ECCV 2016 Workshops, Part II, LNCS 9914, pp. 777–823, 2016. DOI: 10.1007/978-3-319-48881-3 54
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M. Kristan et al. 4
Link¨ oping University, Link¨ oping, Sweden Austrian Institute of Technology, Seibersdorf, Austria 6 ARC Centre of Excellence for Robotic Vision, Brisbane, Australia 7 Aselsan Research Center, Ankara, Turkey 8 ASRI, Seoul, South Korea 9 Australian National University, Canberra, Australia 10 Carnegie Mellon University, Pittsburgh, USA 11 Chinese Academy of Sciences, Beijing, China 12 Dalian University of Technology, Dalian, China 13 Electronics and Telecommunications Research Institute, Seoul, South Korea 14 Fraunhofer IOSB, Karlsruhe, Germany 15 Graz University of Technology, Graz, Austria 16 Hacettepe University, C ¸ ankaya, Turkey 17 Harbin Institute of Technology, Harbin, China 18 Hong Kong Baptist University, Kowloon Tong, China 19 Hong Kong University of Science and Technology, Hong Kong, China 20 Imperial College London, London, England 21 Indian Institute of Space Science and Technology, Thiruvananthapuram, India 22 KAUST, Thuwal, Saudi Arabia 23 Kyiv Polytechnic Institute, Kiev, Ukraine 24 Lehigh University, Bethlehem, USA 25 Marquette University, Milwaukee, USA 26 Middle East Technical University, C ¸ ankaya, Turkey 27 Naval Research Lab, Washington, D.C., USA 28 NAVER Corporation, Seongnam, South Korea 29 Data61/CSIRO, Eveleigh, Australia 30 Parthenope University of Naples, Napoli, Italy 31 POSTECH, Pohang, South Korea 32 Universidad Aut´ onoma de Madrid, Madrid, Spain 33 Universidade Federal de Itajub´ a, Pinheirinho, Brazil 34 University at Albany, Albany, USA 35 University of Chinese Academy of Sciences, Beijing, China 36 University of Isfahan, Isfahan, Iran 37 University of Missouri, Columbia, USA 38 University of Nottingham, Nottingham, England 39 University of Ottawa, Ottawa, Canada 40 University of Oxford, Oxford, England 41 University of Surrey, Guildford, England 42 University of Verona, Verona, Italy 43 Xi’an Jiaotong University, Xi’an, China 44 Zhejiang University, Hangzhou, China 45 Moshanghua Technology Co., Beijing, China 5
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
The Visual Object Tracking VOT2016 Challenge Results
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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
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