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Long-term Tracking in the Wild: A Benchmark [article]

Jack Valmadre, Luca Bertinetto, João F. Henriques, Ran Tao, Andrea Vedaldi, Arnold Smeulders, Philip Torr, Efstratios Gavves
2018 arXiv   pre-print

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https://web.archive.org/web/20200823013218/https://arxiv.org/pdf/1803.09502v2.pdf

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2018 pre-print arXiv:1803.09502v2 fatcat:lhmy4mggbvcuppkgrlvq5hdfue

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2018 pre-print
arXiv:1803.09502v1 fatcat:zyvphd7v4fbq7ixavcxuthcphe
Our goal is to offer the community a large and diverse benchmark to enable the design and evaluation of tracking methods ready to be used "in the wild". The project website is http://oxuva.net  ...  Aiming to address this disparity, we compile a long-term, large-scale tracking dataset of sequences with average length greater than two minutes and with frequent target object disappearance.  ...  In this work, we use YTBB as a source from which we select, curate and further annotate the sequences that constitute our long-term benchmark. Tracking benchmarks.  ... 
arXiv:1803.09502v3 fatcat:b4nxau245ncvzhui2r4lo2vjpq
Open Access Multiple Versions
Citation

Jack Valmadre, Luca Bertinetto, João F. Henriques, Ran Tao, Andrea Vedaldi, Arnold Smeulders, Philip Torr, Efstratios Gavves. "Long-term Tracking in the Wild: A Benchmark." arXiv (2018)

Long-Term Tracking in the Wild: A Benchmark [chapter]

Jack Valmadre, Luca Bertinetto, João F. Henriques, Ran Tao, Andrea Vedaldi, Arnold W. M. Smeulders, Philip H. S. Torr, Efstratios Gavves
2018 Lecture Notes in Computer Science  

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https://web.archive.org/web/20180922013857/http://openaccess.thecvf.com:80/content_ECCV_2018/papers/Efstratios_Gavves_Long-term_Tracking_in_ECCV_2018_paper.pdf

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Our goal is to offer the community a large and diverse benchmark to enable the design and evaluation of tracking methods ready to be used "in the wild".  ...  Aiming to address this disparity, we compile a long-term, large-scale tracking dataset of sequences with average length greater than two minutes and with frequent target object disappearance.  ...  Table 1 . 1 Comparison of the proposed OxUvA long-term tracking benchmark to existing benchmarks.  ... 
doi:10.1007/978-3-030-01219-9_41 fatcat:uxgm26didvh5vbx6jue37ee5ue
Citation

Jack Valmadre, Luca Bertinetto, João F. Henriques, Ran Tao, Andrea Vedaldi, Arnold W. M. Smeulders, Philip H. S. Torr, Efstratios Gavves. "Long-Term Tracking in the Wild: A Benchmark." Lecture Notes in Computer Science (2018) 692-707

Long-term face tracking in the wild using deep learning [article]

Kunlei Zhang, Elaheh Rashedi, Elaheh Barati, Xue-wen Chen
2018 arXiv   pre-print

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https://web.archive.org/web/20200823155246/https://arxiv.org/pdf/1805.07646v1.pdf

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This paper investigates long-term face tracking of a specific person given his/her face image in a single frame as a query in a video stream.  ...  In the proposed system, we present a detection-verification-tracking method (dubbed as 'DVT') which accomplishes the long-term face tracking task through the collaboration of face detection, face verification  ...  Long-term real-time tracking of human faces in the wild is a challenging problem because the video may include frame cuts, sudden appearance changes, long-lasting occlusions, etc.  ... 
arXiv:1805.07646v1 fatcat:7sfohmhcwzho7psip6c6zlmbfq
Citation

Kunlei Zhang, Elaheh Rashedi, Elaheh Barati, Xue-wen Chen. "Long-term face tracking in the wild using deep learning." arXiv (2018)

WILDS: A Benchmark of in-the-Wild Distribution Shifts [article]

Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David (+11 others)
2021 arXiv   pre-print

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https://web.archive.org/web/20210721210453/https://arxiv.org/pdf/2012.07421v3.pdf

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2020 pre-print
arXiv:2012.07421v1 fatcat:cgn7mlkdvrdxzfcf6beckl6obe
2021 pre-print
arXiv:2012.07421v2 fatcat:cikk6ccev5dn3pajvzqkus757q
To address this gap, we present WILDS, a curated benchmark of 10 datasets reflecting a diverse range of distribution shifts that naturally arise in real-world applications, such as shifts across hospitals  ...  Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild.  ...  Acknowledgements Many people generously volunteered their time and expertise to advise us on Wilds.  ... 
arXiv:2012.07421v3 fatcat:bsohmukpszajxeadeo25oxmbs4
Multiple Versions
Citation

Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton A. Earnshaw, Imran S. Haque, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, Percy Liang. "WILDS: A Benchmark of in-the-Wild Distribution Shifts." arXiv (2021)

GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild [article]

Lianghua Huang, Xin Zhao, Kaiqi Huang
2018 arXiv   pre-print

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https://web.archive.org/web/20200907051956/https://arxiv.org/pdf/1810.11981v2.pdf

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2019 accepted
arXiv:1810.11981v3 fatcat:5d42hyht3zcwtdbylvsbw3jkle
2018 pre-print
arXiv:1810.11981v1 fatcat:qacdyzctlndzvlpjfvlk33l6qm
In this work, we introduce a large high-diversity database for generic object tracking, called GOT-10k. GOT-10k is backboned by the semantic hierarchy of WordNet.  ...  The evaluation results suggest that tracking in real-world unconstrained videos is far from being solved, and only 40% of frames are successfully tracked using top ranking trackers.  ...  They include the large scale people and rigid object tracking dataset NUS PRO [14] , long-term aerial tracking dataset UAV123 [12] , color tracking dataset TColor-128 [13] , longterm tracking dataset  ... 
arXiv:1810.11981v2 fatcat:7ckcfssrobby7m5vvqvkv2spxi
Multiple Versions
Citation

Lianghua Huang, Xin Zhao, Kaiqi Huang. "GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild." arXiv (2018)

LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild [article]

Shuang Yang, Yuanhang Zhang, Dalu Feng, Mingmin Yang, Chenhao Wang, Jingyun Xiao, Keyu Long, Shiguang Shan, Xilin Chen
2019 arXiv   pre-print

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https://web.archive.org/web/20200828215245/https://arxiv.org/pdf/1810.06990v6.pdf

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2019 pre-print
arXiv:1810.06990v5 fatcat:qrjv3amvlvbjjm7nyb4of3hjnu
2018 pre-print
arXiv:1810.06990v2 fatcat:eh2pm5mqy5dbpmn5gkief5bue4
2018 pre-print
arXiv:1810.06990v3 fatcat:hoe3iw6dzvb5tlkkxa5odwdu54
2018 pre-print
arXiv:1810.06990v1 fatcat:vwpxon5d5jawlahtmsod6jbhmi
2018 pre-print
arXiv:1810.06990v4 fatcat:fksj6lb7tvcrpjaxxq472hqo3q
We present a naturally-distributed large-scale benchmark for lip reading in the wild, named LRW-1000, which contains 1,000 classes with 718,018 samples from more than 2,000 individual speakers.  ...  It has shown a large variation in this benchmark in several aspects, including the number of samples in each class, video resolution, lighting conditions, and speakers' attributes such as pose, age, gender  ...  This research was supported in part by the National Key R&D Program of China (grant 2017YFA0700800), Natural Science Foundation of China (grants 61702486, 61876171).  ... 
arXiv:1810.06990v6 fatcat:tytqyjvn6bfavjmhkazeg3wydy
Multiple Versions
Citation

Shuang Yang, Yuanhang Zhang, Dalu Feng, Mingmin Yang, Chenhao Wang, Jingyun Xiao, Keyu Long, Shiguang Shan, Xilin Chen. "LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild." arXiv (2019)

AnimalTrack: A Large-scale Benchmark for Multi-Animal Tracking in the Wild [article]

Libo Zhang, Junyuan Gao, Zhen Xiao, Heng Fan
2022 arXiv   pre-print

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https://web.archive.org/web/20220506163158/https://arxiv.org/pdf/2205.00158v1.pdf

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To address this problem, we introduce AnimalTrack, a large-scale benchmark for multi-animal tracking in the wild.  ...  To our best knowledge, AnimalTrack is the first benchmark dedicated to multi-animal tracking.  ...  The work of [38] presents a new dataset for animal localization in the wild. A benchmark for tiger re-identification is proposed in [27] .  ... 
arXiv:2205.00158v1 fatcat:a3qux6crazbvvlbo2urzgoz4ge
Citation

Libo Zhang, Junyuan Gao, Zhen Xiao, Heng Fan. "AnimalTrack: A Large-scale Benchmark for Multi-Animal Tracking in the Wild." arXiv (2022)

AP-10K: A Benchmark for Animal Pose Estimation in the Wild [article]

Hang Yu, Yufei Xu, Jing Zhang, Wei Zhao, Ziyu Guan, Dacheng Tao
2021 arXiv   pre-print

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https://web.archive.org/web/20211108132631/https://arxiv.org/pdf/2108.12617v2.pdf

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In this paper, we propose AP-10K, the first large-scale benchmark for mammal animal pose estimation, to facilitate the research in animal pose estimation.  ...  The experimental results provide sound empirical evidence on the superiority of learning from diverse animals species in terms of both accuracy and generalization ability.  ...  Long-tail property As shown in Figure 3 , the number of images in each family of AP-10K has a long-tail distribution, which reflects the true distribution of animals in the wild due to the commonness  ... 
arXiv:2108.12617v2 fatcat:vmxn2rm5ojdjrlarmygbx6di4q
Citation

Hang Yu, Yufei Xu, Jing Zhang, Wei Zhao, Ziyu Guan, Dacheng Tao. "AP-10K: A Benchmark for Animal Pose Estimation in the Wild." arXiv (2021)

TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild [chapter]

Matthias Müller, Adel Bibi, Silvio Giancola, Salman Alsubaihi, Bernard Ghanem
2018 Lecture Notes in Computer Science  

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https://web.archive.org/web/20180922014825/http://openaccess.thecvf.com:80/content_ECCV_2018/papers/Matthias_Muller_TrackingNet_A_Large-Scale_ECCV_2018_paper.pdf

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In this work, we present TrackingNet, the first large-scale dataset and benchmark for object tracking in the wild.  ...  We provide an extensive benchmark on TrackingNet by evaluating more than 20 trackers. Our results suggest that object tracking in the wild is far from being solved.  ...  Introduction Object tracking is a common task in computer vision, with a long history spanning decades [50, 30, 44] .  ... 
doi:10.1007/978-3-030-01246-5_19 fatcat:ls4qv2re2nekpgcc5sytb2ae2u
Citation

Matthias Müller, Adel Bibi, Silvio Giancola, Salman Alsubaihi, Bernard Ghanem. "TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild." Lecture Notes in Computer Science (2018) 310-327

Gait Recognition in the Wild with Dense 3D Representations and A Benchmark [article]

Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei
2022 arXiv   pre-print

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https://web.archive.org/web/20220508082133/https://arxiv.org/pdf/2204.02569v1.pdf

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recognition in the wild.  ...  Therefore, this paper aims to explore dense 3D representations for gait recognition in the wild, which is a practical yet neglected problem.  ...  learning on the temporal dimension of the sequence to learn both long-term and short-term motion for gait recognition.  ... 
arXiv:2204.02569v1 fatcat:uiczctllyfcaxbgvmxarksloru
Open Access
Citation

Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei. "Gait Recognition in the Wild with Dense 3D Representations and A Benchmark." arXiv (2022)

A Benchmark and Simulator for UAV Tracking [chapter]

Matthias Mueller, Neil Smith, Bernard Ghanem
2016 Lecture Notes in Computer Science  

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Among the compared trackers, we determine which ones are the most suitable for UAV tracking both in terms of tracking accuracy and run-time.  ...  In this paper, we propose a new aerial video dataset and benchmark for low altitude UAV target tracking, as well as, a photorealistic UAV simulator that can be coupled with tracking methods.  ...  Research in this paper was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research.  ... 
doi:10.1007/978-3-319-46448-0_27 fatcat:yeahla6qwjfdho4rhzciqgwkxa
Citation

Matthias Mueller, Neil Smith, Bernard Ghanem. "A Benchmark and Simulator for UAV Tracking." Lecture Notes in Computer Science (2016) 445-461

TAO: A Large-Scale Benchmark for Tracking Any Object [article]

Achal Dave, Tarasha Khurana, Pavel Tokmakov, Cordelia Schmid, Deva Ramanan
2020 arXiv   pre-print

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For many years, multi-object tracking benchmarks have focused on a handful of categories.  ...  It consists of 2,907 high resolution videos, captured in diverse environments, which are half a minute long on average.  ...  We compare the statistics of TAO to existing multi-object tracking benchmarks in Table 1 and Figure 1 , and demonstrate that it improves upon them both in terms of complexity and in terms of diversity  ... 
arXiv:2005.10356v1 fatcat:xvvtumwudvcwnbk7g7pu3k374a
Citation

Achal Dave, Tarasha Khurana, Pavel Tokmakov, Cordelia Schmid, Deva Ramanan. "TAO: A Large-Scale Benchmark for Tracking Any Object." arXiv (2020)

MOT16: A Benchmark for Multi-Object Tracking [article]

Anton Milan, Laura Leal-Taixe, Ian Reid, Stefan Roth, Konrad Schindler
2016 arXiv   pre-print

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https://web.archive.org/web/20200928162702/https://arxiv.org/pdf/1603.00831v2.pdf

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2016 pre-print
arXiv:1603.00831v1 fatcat:lopptzrmebhbhadn32uknifpzi
The first release of the benchmark focuses on multiple people tracking, since pedestrians are by far the most studied object in the tracking community.  ...  Recently, a new benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal of collecting existing and new data and creating a framework for the standardized evaluation of multiple  ...  We would like to specially acknowledge Siyu Tang, Sarah Becker, Andreas Lin and Kinga Milan for their help in the annotation process.  ... 
arXiv:1603.00831v2 fatcat:o257r5smrbg4disft45yw53s2u
Multiple Versions
Citation

Anton Milan, Laura Leal-Taixe, Ian Reid, Stefan Roth, Konrad Schindler. "MOT16: A Benchmark for Multi-Object Tracking." arXiv (2016)

LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking [article]

Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao, Haibin Ling
2019 arXiv   pre-print

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2018 pre-print
arXiv:1809.07845v1 fatcat:mai65ixlyzggniad2mkib3io3i
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.  ...  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.  ...  Long-term tracking in the wild: A benchmark. In ECCV, 2018. 2, 3 [52] Yi Wu, Jongwoo Lim, and Ming-Hsuan Yang. Online object tracking: A benchmark.  ... 
arXiv:1809.07845v2 fatcat:jwejl7d7hzgwtjgb3xu75und4q
Multiple Versions
Citation

Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao, Haibin Ling. "LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking." arXiv (2019)

LaSOT: A High-quality Large-scale Single Object Tracking Benchmark [article]

Heng Fan, Hexin Bai, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Harshit, Mingzhen Huang, Juehuan Liu, Yong Xu, Chunyuan Liao (+2 others)
2020 arXiv   pre-print

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2020 pre-print
arXiv:2009.03465v1 fatcat:xfnmavbckff4jmi2u37rnoxypu
2020 pre-print
arXiv:2009.03465v2 fatcat:hzg5yorgi5cnddhmw6mkwxocp4
These longer video lengths allow for the assessment of long-term trackers.  ...  This makes LaSOT, to our knowledge, the largest densely annotated tracking benchmark.  ...  Acknowledgements We thank the anonymous reviewers for insightful suggestions, and Jeremy Chu for proofreading the final draft.  ... 
arXiv:2009.03465v3 fatcat:bfe4qmryfbaslozwmcvlsm7n7u
Multiple Versions
Citation

Heng Fan, Hexin Bai, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Harshit, Mingzhen Huang, Juehuan Liu, Yong Xu, Chunyuan Liao, Lin Yuan, Haibin Ling. "LaSOT: A High-quality Large-scale Single Object Tracking Benchmark." arXiv (2020)

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