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The Visual Object Tracking VOT2015 Challenge Results

Matej Kristan, Jiri Matas, Ales Leonardis, Michael Felsberg, Luka Cehovin, Gustavo Fernández, Tomás Vojír, Gustav Häger, Georg Nebehay, Roman P. Pflugfelder
2015 2015 IEEE International Conference on Computer Vision Workshop (ICCVW)  
The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative.  ...  The dataset, the evaluation kit and the results are publicly available at the challenge website 1 .  ...  This paper presents the VOT2017 challenge, organized in conjunction with the ICCV2017 Visual Object Tracking workshop, and the results obtained.  ... 
doi:10.1109/iccvw.2015.79 dblp:conf/iccvw/KristanMLFCFVHN15 fatcat:7wvdw2bm3jcoxdwr2ehp4vese4

Visual Tracking Based on Complementary Learners with Distractor Handling

Suryo Adhi Wibowo, Hansoo Lee, Eun Kyeong Kim, Sungshin Kim
2017 Mathematical Problems in Engineering  
The representation of the object is an important factor in building a robust visual object tracking algorithm.  ...  This decision depends on the result obtained from the distractor detection process. Experiments were performed on the widely used VOT2014 and VOT2015 benchmark datasets.  ...  Several decades ago, to solve challenging problems in visual tracking, researchers used a color histogram [1] to represent the target object.  ... 
doi:10.1155/2017/5295601 fatcat:k6sxtdue6fgmdbzssycpweyr74

Deep Learning Trackers Review and Challenge

Yongxiang Gu, Beijing Chen, Xu Cheng, Yifeng Zhang, Jingang Shi
2019 Journal of Information Hiding and Privacy Protection  
Matas, J.; Leonardis, A.; Felsberg, M.; Cehovin, L. et al. (2015): The visual object tracking VOT2015 challenge results. Jiang, Y.  ...  Recently, deep learning has achieved great success in visual tracking. The goal of this paper is to review the state-of-the-art tracking methods based on deep learning.  ...  The major difference between VOT2015 and OTB-100 is that the VOT2015 challenge provides a reinitialization protocol (i.e., trackers are reset with ground-truths in the middle of evaluation if tracking  ... 
doi:10.32604/jihpp.2019.05938 fatcat:z2kq47sl25fz7fykzdhfozysge

The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results

Michael Felsberg, Amanda Berg, Gustav Häger, Jörgen Ahlberg, Matej Kristan, Jiri Matas, Ales Leonardis, Luka Cehovin, Gustavo Fernández, Tomás Vojír, Georg Nebehay, Roman P. Pflugfelder
2015 2015 IEEE International Conference on Computer Vision Workshop (ICCVW)  
The Thermal Infrared Visual Object Tracking challenge 2015, VOT-TIR2015, aims at comparing short-term singleobject visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned  ...  VOT-TIR2015 is the first benchmark on shortterm tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix.  ...  This paper describes the first thermal infrared (TIR), short-term tracking challenge, the Visual Object Tracking TIR (VOT-TIR2015) challenge, and the results obtained.  ... 
doi:10.1109/iccvw.2015.86 dblp:conf/iccvw/FelsbergBHAKMLC15 fatcat:lu5tvcxj2rhede2d3kpjvz3ghy

The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results [chapter]

Michael Felsberg, Matej Kristan, Jiři Matas, Aleš Leonardis, Roman Pflugfelder, Gustav Häger, Amanda Berg, Abdelrahman Eldesokey, Jörgen Ahlberg, Luka Čehovin, Tomáš Vojír̃, Alan Lukežič (+65 others)
2016 Lecture Notes in Computer Science  
The Thermal Infrared Visual Object Tracking challenge 2015, VOT-TIR2015, aims at comparing short-term singleobject visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned  ...  VOT-TIR2015 is the first benchmark on shortterm tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix.  ...  This paper describes the first thermal infrared (TIR), short-term tracking challenge, the Visual Object Tracking TIR (VOT-TIR2015) challenge, and the results obtained.  ... 
doi:10.1007/978-3-319-48881-3_55 fatcat:ifztdsww4jdn3gkwgy6x5v7huy

The Visual Object Tracking VOT2014 Challenge Results [chapter]

Matej Kristan, Roman Pflugfelder, Aleš Leonardis, Jiri Matas, Luka Čehovin, Georg Nebehay, Tomáš Vojíř, Gustavo Fernández, Alan Lukežič, Aleksandar Dimitriev, Alfredo Petrosino, Amir Saffari (+45 others)
2015 Lecture Notes in Computer Science  
The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance.  ...  The dataset, the evaluation kit as well as the results are publicly available at the challenge website 1 .  ...  Acknowledgements This work was supported in part by the following research programs and projects: Slovenian research agency research programs P2-0214, P2-0094, Slovenian research agency projects J2-4284  ... 
doi:10.1007/978-3-319-16181-5_14 fatcat:oawrb5vmxvdeji675oyluf3dym

The Visual Object Tracking VOT2016 Challenge Results [chapter]

Matej Kristan, Aleš Leonardis, Jiři Matas, Michael Felsberg, Roman Pflugfelder, Luka Čehovin, Tomáš Vojír̃, Gustav Häger, Alan Lukežič, Gustavo Fernández, Abhinav Gupta, Alfredo Petrosino (+127 others)
2016 Lecture Notes in Computer Science  
The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply prelearned models of object appearance.  ...  The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending The Visual Object Tracking VOT2016 challenge results  ...  Acknowledgements This work was supported in part by the following research programs and projects: Slovenian research agency research programs P2-0214, P2-0094, Slovenian research agency projects J2-4284  ... 
doi:10.1007/978-3-319-48881-3_54 fatcat:2hbd6hnvtnbfzn7vwwgqaqg6o4

The Visual Object Tracking VOT2017 Challenge Results

Matej Kristan, Ales Leonardis, Jiri Matas, Michael Felsberg, Roman Pflugfelder, Luka Cehovin Zajc, Tomas Vojir, Gustav Hager, Alan Lukezic, Abdelrahman Eldesokey, Gustavo Fernandez, Alvaro Garcia-Martin (+92 others)
2017 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)  
Acknowledgements This work was supported in part by the following research programs and projects: Slovenian research agency research programs P2-0214, P2-0094, Slovenian research agency projects J2-4284  ...  This paper discusses the VOT2015 challenge organized in conjunction with the ICCV2015 Visual object tracking workshop and the results obtained.  ...  Videos (ALOV) [61] and the Visual object tracking challenge (VOT) [38, 36, 34] .  ... 
doi:10.1109/iccvw.2017.230 dblp:conf/iccvw/KristanLMFPZVHL17 fatcat:3ik6smlk2belrhxick2vsn5rbu

SANet: Structure-Aware Network for Visual Tracking

Heng Fan, Haibin Ling
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Extensive experimental results on three large-scale benchmarks, OTB100, TC-128 and VOT2015, show that the proposed algorithm outperforms other state-of-the-art methods.  ...  Convolutional neural network (CNN) has drawn increasing interest in visual tracking owing to its powerfulness in feature extraction.  ...  For visual tracking, one major challenge is to separate object from similar distractors of intra-class.  ... 
doi:10.1109/cvprw.2017.275 dblp:conf/cvpr/FanL17 fatcat:gnq7chdzovf3foneczsf2ih32e

Channel Coded Distribution Field Tracking for Thermal Infrared Imagery

Amanda Berg, Jorgen Ahlberg, Michael Felsberg
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
We address short-term, single-object tracking, a topic that is currently seeing fast progress for visual video, for the case of thermal infrared (TIR) imagery.  ...  The proposed tracker is evaluated on the VOT-TIR2015 and VOT2015 datasets using the VOT evaluation toolkit and a comparison of relative ranking of all common participating trackers in the challenges is  ...  Acknowledgements The research was funded by the Swedish Research Council through the project Learning Systems for Remote Thermography, grant no.  ... 
doi:10.1109/cvprw.2016.158 dblp:conf/cvpr/BergAF16 fatcat:jlvtzzisy5cgzastvgt3zg32gy

SANet: Structure-Aware Network for Visual Tracking [article]

Heng Fan, Haibin Ling
2017 arXiv   pre-print
Convolutional neural network (CNN) has drawn increasing interest in visual tracking owing to its powerfulness in feature extraction.  ...  Extensive experiments on three benchmarks, OTB100, TC-128 and VOT2015, show that the proposed algorithm outperforms other methods.  ...  For visual tracking, one major challenge is to separate object from similar distractors of intra-class.  ... 
arXiv:1611.06878v3 fatcat:mmrutzuhprespnnrgc4ksdec4a

Patch-based adaptive weighting with segmentation and scale (PAWSS) for visual tracking [article]

Xiaofei Du, Alessio Dore, Danail Stoyanov
2017 arXiv   pre-print
Experimentally, we evaluate our approach on the online tracking benchmark (OTB) dataset and Visual Object Tracking (VOT) challenge datasets.  ...  Tracking-by-detection algorithms are widely used for visual tracking, where the problem is treated as a classification task where an object model is updated over time using online learning techniques.  ...  The work was supported by the EP-SRC (EP/N013220/1, EP/N022750/1, EP/N027078/1, NS/A000027/1, EP/P012841/1), The Wellcome Trust (WT101957, 201080/Z/16/Z) and the EU-Horizon2020 project EndoVESPA (H2020  ... 
arXiv:1708.01179v1 fatcat:jlx2y42dg5bj7ktgxni6ethwle

High Performance Visual Object Tracking with Unified Convolutional Networks [article]

Zheng Zhu, Wei Zou, Guan Huang, Dalong Du, Chang Huang
2019 arXiv   pre-print
Experiments are performed on four challenging tracking datasets: OTB2013, OTB2015, VOT2015 and VOT2016.  ...  Nonetheless, the chosen CNN features are always pre-trained in different tasks and individual components in tracking systems are learned separately, thus the achieved tracking performance may be suboptimal  ...  Qualitative results To visualize the superiority of our framework on tracking performance, we show examples of UCT results compared with recent trackers on challenging sample videos.  ... 
arXiv:1908.09445v1 fatcat:w5nhmig2hnewvl53ll7spb4pu4

Accurate Positioning Siamese Network for Real-Time Object Tracking

Lijun Zhou, Xuwen Yao, Jianlin Zhang
2019 IEEE Access  
A number of experiments were conducted on five challenging tracking datasets: OTB50, OTB2013, OTB2015, VOT2015, and VOT2016, and the proposed method achieved excellent results on these benchmarks.  ...  This approach can better estimate the search candidate area of the object in the current frame from the previous tracking position and can keep tracking well for the fast moving object.  ...  The authors express our thanks for the experiment equipment provided by the lab.  ... 
doi:10.1109/access.2019.2924147 fatcat:gfi4iaklqvhspmcn3jnf4sinma

End-to-end Flow Correlation Tracking with Spatial-temporal Attention [article]

Zheng Zhu, Wei Wu, Wei Zou, Junjie Yan
2018 arXiv   pre-print
Extensive experiments are performed on four challenging tracking datasets: OTB2013, OTB2015, VOT2015 and VOT2016, and the proposed method achieves superior results on these benchmarks.  ...  The lack of temporal information degrades the tracking performance during challenges such as partial occlusion and deformation.  ...  Results comparisons of our approach with three state-of-the-art trackers in the challenging scenario is shown in Figure 1 Results on VOT The Visual Object Tracking (VOT) challenges are wellknown competitions  ... 
arXiv:1711.01124v4 fatcat:wd3zarrdpfepvojiuiu5vgmnvu
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