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LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking
Heng Fan,Liting Lin,Fan Yang,Peng Chu,Ge Deng,Sijia Yu,Hexin Bai,Yong Xu,Chunyuan Liao,Haibin Ling +9 more
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TLDR
The LaSOT benchmark as discussed by the authors provides a high-quality benchmark for large-scale single object tracking, which consists of 1,400 sequences with more than 3.5M frames in total.Abstract:
In this paper, we present LaSOT, a high-quality benchmark for Large-scale Single Object Tracking. LaSOT consists of 1,400 sequences with more than 3.5M frames in total. Each frame in these sequences is carefully and manually annotated with a bounding box, making LaSOT the largest, to the best of our knowledge, densely annotated tracking benchmark. The average video length of LaSOT is more than 2,500 frames, and each sequence comprises various challenges deriving from the wild where target objects may disappear and re-appear again in the view. By releasing LaSOT, we expect to provide the community with a large-scale dedicated benchmark with high quality for both the training of deep trackers and the veritable evaluation of tracking algorithms. Moreover, considering the close connections of visual appearance and natural language, we enrich LaSOT by providing additional language specification, aiming at encouraging the exploration of natural linguistic feature for tracking. A thorough experimental evaluation of 35 tracking algorithms on LaSOT is presented with detailed analysis, and the results demonstrate that there is still a big room for improvements.read more
Citations
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Journal ArticleDOI
GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild
TL;DR: A large tracking database that offers an unprecedentedly wide coverage of common moving objects in the wild, called GOT-10k, and the first video trajectory dataset that uses the semantic hierarchy of WordNet to guide class population, which ensures a comprehensive and relatively unbiased coverage of diverse moving objects.
Proceedings ArticleDOI
ATOM: Accurate Tracking by Overlap Maximization
TL;DR: ATOM as discussed by the authors proposes a novel tracking architecture consisting of dedicated target estimation and classification components, which is trained to predict the overlap between the target object and an estimated bounding box.
Proceedings ArticleDOI
Siam R-CNN: Visual Tracking by Re-Detection
TL;DR: This work presents Siam R-CNN, a Siamese re-detection architecture which unleashes the full power of two-stage object detection approaches for visual object tracking, and combines this with a novel tracklet-based dynamic programming algorithm to model the full history of both the object to be tracked and potential distractor objects.
Proceedings ArticleDOI
The Seventh Visual Object Tracking VOT2019 Challenge Results
Matej Kristan,Amanda Berg,Linyu Zheng,Litu Rout,Luc Van Gool,Luca Bertinetto,Martin Danelljan,Matteo Dunnhofer,Meng Ni,Min Young Kim,Ming Tang,Ming-Hsuan Yang,Abdelrahman Eldesokey,Naveen Paluru,Niki Martinel,Pengfei Xu,Pengfei Zhang,Pengkun Zheng,Pengyu Zhang,Philip H. S. Torr,Qi Zhang Qiang Wang,Qing Guo,Radu Timofte,Jani Käpylä,Rama Krishna Sai Subrahmanyam Gorthi,Richard M. Everson,Ruize Han,Ruohan Zhang,Shan You,Shaochuan Zhao,Shengwei Zhao,Shihu Li,Shikun Li,Shiming Ge,Gustavo Fernandez,Shuai Bai,Shuosen Guan,Tengfei Xing,Tianyang Xu,Tianyu Yang,Ting Zhang,Tomas Vojir,Wei Feng,Weiming Hu,Weizhao Wang,Abel Gonzalez-Garcia,Wenjie Tang,Wenjun Zeng,Wenyu Liu,Xi Chen,Xi Qiu,Xiang Bai,Xiaojun Wu,Xiaoyun Yang,Xier Chen,Xin Li,Alireza Memarmoghadam,Xing Sun,Xingyu Chen,Xinmei Tian,Xu Tang,Xue-Feng Zhu,Yan Huang,Yanan Chen,Yanchao Lian,Yang Gu,Yang Liu,Andong Lu,Chen Yanjie,Yi Zhang,Yinda Xu,Yingming Wang,Yingping Li,Yu Zhou,Yuan Dong,Yufei Xu,Yunhua Zhang,Yunkun Li,Anfeng He,Zeyu Wang Zhao Luo,Zhaoliang Zhang,Zhen-Hua Feng,Zhenyu He,Zhichao Song,Zhihao Chen,Zhipeng Zhang,Zhirong Wu,Zhiwei Xiong,Zhongjian Huang,Anton Varfolomieiev,Zhu Teng,Zihan Ni,Antoni Chan,Jiri Matas,Ardhendu Shekhar Tripathi,Arnold W. M. Smeulders,Bala Suraj Pedasingu,Bao Xin Chen,Baopeng Zhang,Baoyuan Wu,Bi Li,Bin He,Bin Yan,Bing Bai,Ales Leonardis,Bing Li,Bo Li,Byeong Hak Kim,Chao Ma,Chen Fang,Chen Qian,Cheng Chen,Chenglong Li,Chengquan Zhang,Chi-Yi Tsai,Michael Felsberg,Chong Luo,Christian Micheloni,Chunhui Zhang,Dacheng Tao,Deepak K. Gupta,Dejia Song,Dong Wang,Efstratios Gavves,Eunu Yi,Fahad Shahbaz Khan,Roman Pflugfelder,Fangyi Zhang,Fei Wang,Fei Zhao,George De Ath,Goutam Bhat,Guangqi Chen,Guangting Wang,Guoxuan Li,Hakan Cevikalp,Hao Du,Joni-Kristian Kamarainen,Haojie Zhao,Hasan Saribas,Ho Min Jung,Hongliang Bai,Hongyuan Yu,Houwen Peng,Huchuan Lu,Hui Li,Jiakun Li,Luka Čehovin Zajc,Jianhua Li,Jianlong Fu,Jie Chen,Jie Gao,Jie Zhao,Jin Tang,Jing Li,Jingjing Wu,Jingtuo Liu,Jinqiao Wang,Ondrej Drbohlav,Jinqing Qi,Jinyue Zhang,John K. Tsotsos,Jong Hyuk Lee,Joost van de Weijer,Josef Kittler,Jun Ha Lee,Junfei Zhuang,Kangkai Zhang,Kangkang Wang,Alan Lukezic,Kenan Dai,Lei Chen,Lei Liu,Leida Guo,Li Zhang,Liang Wang,Liangliang Wang,Lichao Zhang,Lijun Wang,Lijun Zhou +179 more
TL;DR: 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.
Proceedings ArticleDOI
Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking
Heng Fan,Haibin Ling +1 more
TL;DR: C-RPN as discussed by the authors proposes a multi-stage tracking framework, which consists of a sequence of RPNs cascaded from deep high-level to shallow low-level layers in a Siamese network.
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