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id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year
0,,TownCentre,oxford_town_centre,0.0,0.0,,,,main,,Stable multi-target tracking in real-time surveillance video,2011
1,United States,TownCentre,oxford_town_centre,40.4441619,-79.94272826,Carnegie Mellon University,edu,03ae36b2ed0215b15c5bc7d42fbe20b1491e551a,citation,http://vishnu.boddeti.net/papers/cvpr-2015-abstract.pdf,Learning scene-specific pedestrian detectors without real data,2015
2,United States,TownCentre,oxford_town_centre,37.3675905,-121.9133491,Sony,company,03ae36b2ed0215b15c5bc7d42fbe20b1491e551a,citation,http://vishnu.boddeti.net/papers/cvpr-2015-abstract.pdf,Learning scene-specific pedestrian detectors without real data,2015
3,United States,TownCentre,oxford_town_centre,42.3504253,-71.10056114,Boston University,edu,9363bf52a5bb2ac94bf247ca56e7cf55fb29ee4e,citation,http://cs-www.bu.edu/groups/ivc/software/TrackerHierarchy/AVSS2012_TrackerHierarchy.pdf,Online Multi-person Tracking by Tracker Hierarchy,2012
4,United States,TownCentre,oxford_town_centre,28.59899755,-81.19712501,University of Central Florida,edu,80b41fb824f3751b03017bf7ec8c5f71b7e214b2,citation,http://crcv-web.eecs.ucf.edu/papers/cvpr2013/CVPR2013_Yang_FinalVersion_HumanDetection.pdf,Semi-supervised Learning of Feature Hierarchies for Object Detection in a Video,2013
5,United States,TownCentre,oxford_town_centre,39.2899685,-76.62196103,University of Maryland,edu,2b9410889dc6870cc6e0476dbc681049b28ccacb,citation,http://drum.lib.umd.edu/bitstream/1903/13339/1/CS-TR-5018.pdf,Learning to Detect Carried Objects with Minimal Supervision,2013
6,United States,TownCentre,oxford_town_centre,28.59899755,-81.19712501,University of Central Florida,edu,5369b021f2abf5daa77fa5602569bb3b8bb18546,citation,http://crcv-web.eecs.ucf.edu/papers/cvpr2015/AfshinDehghan_GMMCP_CVPR15.pdf,GMMCP tracker: Globally optimal Generalized Maximum Multi Clique problem for multiple object tracking,2015
7,United States,TownCentre,oxford_town_centre,28.59899755,-81.19712501,University of Central Florida,edu,076fd6fd85b93858155a1c775f1897f83d52b4c2,citation,http://crcv-web.eecs.ucf.edu/papers/cvpr2013/CVPR13_final_guang.pdf,Improving an Object Detector and Extracting Regions Using Superpixels,2013
8,United Kingdom,TownCentre,oxford_town_centre,55.91029135,-3.32345777,Heriot-Watt University,edu,b02581323ad03125e9b18d74ba0c1909d6485dda,citation,https://pure.qub.ac.uk/portal/files/57462725/Anomaly1_s2.0_S0167865513004625_main.pdf,Contextual anomaly detection in crowded surveillance scenes,2014
9,United Kingdom,TownCentre,oxford_town_centre,51.7534538,-1.25400997,University of Oxford,edu,184c3e66a746376716d5e816d95e1a7cb8e04390,citation,http://ben.benfold.com/docs/benfold_reid_iccv2011-poster.pdf,Unsupervised learning of a scene-specific coarse gaze estimator,2011
10,United Kingdom,TownCentre,oxford_town_centre,51.7520209,-1.2577263,"Oxford, UK",edu,184c3e66a746376716d5e816d95e1a7cb8e04390,citation,http://ben.benfold.com/docs/benfold_reid_iccv2011-poster.pdf,Unsupervised learning of a scene-specific coarse gaze estimator,2011
11,Israel,TownCentre,oxford_town_centre,31.262218,34.801461,Ben-Gurion University,edu,880e232f260b0f9d649a4e6408b1cf82f270bd6d,citation,http://www.cs.bgu.ac.il/~ben-shahar/Publications/2013-Ben_Ari_and_Ben_Shahar-A_Computationally_Efficient_Tracker_with_Direct_Appearance-Kinematic_Measure_and_Adaptive%20Kalman_Filter.pdf,A computationally efficient tracker with direct appearance-kinematic measure and adaptive Kalman filter,2013
12,Israel,TownCentre,oxford_town_centre,31.8878767,34.7359885,"Orbotech Ltd., Yavne, Israel",company,880e232f260b0f9d649a4e6408b1cf82f270bd6d,citation,http://www.cs.bgu.ac.il/~ben-shahar/Publications/2013-Ben_Ari_and_Ben_Shahar-A_Computationally_Efficient_Tracker_with_Direct_Appearance-Kinematic_Measure_and_Adaptive%20Kalman_Filter.pdf,A computationally efficient tracker with direct appearance-kinematic measure and adaptive Kalman filter,2013
13,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,3e0db33884ca8c756b26dc0df85c498c18d5f2ec,citation,http://is.tuebingen.mpg.de/uploads_file/attachment/attachment/137/LeaPonRos11SocialLP.pdf,Exploiting pedestrian interaction via global optimization and social behaviors,2011
14,United States,TownCentre,oxford_town_centre,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,b28eb219db9370cf20063288225cc2f3e6e5f984,citation,http://faculty.ucmerced.edu/mhyang/papers/iccv15_pose.pdf,Fast and Accurate Head Pose Estimation via Random Projection Forests,2015
15,United States,TownCentre,oxford_town_centre,37.3641651,-120.4254615,University of California at Merced,edu,b28eb219db9370cf20063288225cc2f3e6e5f984,citation,http://faculty.ucmerced.edu/mhyang/papers/iccv15_pose.pdf,Fast and Accurate Head Pose Estimation via Random Projection Forests,2015
16,Austria,TownCentre,oxford_town_centre,47.05821,15.46019568,Graz University of Technology,edu,356ec17af375b63a015d590562381a62f352f7d5,citation,http://lrs.icg.tugraz.at/pubs/possegger_cvpr14.pdf,Occlusion Geodesics for Online Multi-object Tracking,2014
17,United States,TownCentre,oxford_town_centre,45.57022705,-122.63709346,Concordia University,edu,b53289f3f3b17dad91fa4fd25d09fdbc14f8c8cc,citation,http://faculty.ucmerced.edu/mhyang/papers/cviu16_MOT.pdf,Online multi-object tracking via robust collaborative model and sample selection,2017
18,United States,TownCentre,oxford_town_centre,37.8718992,-122.2585399,University of California,edu,b53289f3f3b17dad91fa4fd25d09fdbc14f8c8cc,citation,http://faculty.ucmerced.edu/mhyang/papers/cviu16_MOT.pdf,Online multi-object tracking via robust collaborative model and sample selection,2017
19,United States,TownCentre,oxford_town_centre,28.59899755,-81.19712501,University of Central Florida,edu,920246280e7e70900762ddfa7c41a79ec4517350,citation,http://crcv-web.eecs.ucf.edu/papers/eccv2012/MPMPT-ECCV12.pdf,(MP) 2 T: multiple people multiple parts tracker,2012
20,United States,TownCentre,oxford_town_centre,37.8718992,-122.2585399,University of California,edu,14d5bd23667db4413a7f362565be21d462d3fc93,citation,http://alumni.cs.ucr.edu/~zqin001/cvpr2014.pdf,An Online Learned Elementary Grouping Model for Multi-target Tracking,2014
21,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,9070045c1a9564a5f25b42f3facc7edf4c302483,citation,http://virtualhumans.mpi-inf.mpg.de/papers/lealPonsmollICCVW2011/lealPonsmollICCVW2011.pdf,Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker,2011
22,Singapore,TownCentre,oxford_town_centre,1.3484104,103.68297965,Nanyang Technological University,edu,2323cb559c9e18673db836ffc283c27e4a002ed9,citation,http://arxiv.org/pdf/1605.04502v1.pdf,Joint Learning of Convolutional Neural Networks and Temporally Constrained Metrics for Tracklet Association,2016
23,China,TownCentre,oxford_town_centre,39.905838,116.375516,"Huawei Technologies, Beijing, China",company,434627a03d4433b0df03058724524c3ac1c07478,citation,http://jianghz.com/pubs/mtt_tip_final.pdf,Online Multi-Target Tracking With Unified Handling of Complex Scenarios,2015
24,China,TownCentre,oxford_town_centre,34.250803,108.983693,Xi’an Jiaotong University,edu,434627a03d4433b0df03058724524c3ac1c07478,citation,http://jianghz.com/pubs/mtt_tip_final.pdf,Online Multi-Target Tracking With Unified Handling of Complex Scenarios,2015
25,United States,TownCentre,oxford_town_centre,28.59899755,-81.19712501,University of Central Florida,edu,084352b63e98d3b3310521fb3bda8cb4a77a0254,citation,http://crcv.ucf.edu/papers/1439.pdf,Part-based multiple-person tracking with partial occlusion handling,2012
26,United States,TownCentre,oxford_town_centre,39.5469449,-119.81346566,University of Nevada,edu,084352b63e98d3b3310521fb3bda8cb4a77a0254,citation,http://crcv.ucf.edu/papers/1439.pdf,Part-based multiple-person tracking with partial occlusion handling,2012
27,United Kingdom,TownCentre,oxford_town_centre,55.7782474,-4.1040988,University of the West of Scotland,edu,32b9be86de4f82c5a43da2a1a0a892515da8910d,citation,http://users.informatik.haw-hamburg.de/~ubicomp/arbeiten/papers/ICISP2014.pdf,Robust False Positive Detection for Real-Time Multi-target Tracking,2014
28,Italy,TownCentre,oxford_town_centre,43.7776426,11.259765,"Università degli Studi di Firenze, Firenze",edu,2914a20df10f3bb55c5d4764ece85101c1a3e5a8,citation,http://www.micc.unifi.it/seidenari/wp-content/papercite-data/pdf/icpr_16.pdf,User interest profiling using tracking-free coarse gaze estimation,2016
29,United States,TownCentre,oxford_town_centre,40.4441619,-79.94272826,Carnegie Mellon University,edu,1f4fed0183048d9014e22a72fd50e1e5fbe0777c,citation,https://pdfs.semanticscholar.org/6b7b/1760ed23ef15ec210b2d6795fdf9ad36d0e2.pdf,A Game-Theoretic Approach to Multi-Pedestrian Activity Forecasting,2016
30,United States,TownCentre,oxford_town_centre,37.43131385,-122.16936535,Stanford University,edu,1f4fed0183048d9014e22a72fd50e1e5fbe0777c,citation,https://pdfs.semanticscholar.org/6b7b/1760ed23ef15ec210b2d6795fdf9ad36d0e2.pdf,A Game-Theoretic Approach to Multi-Pedestrian Activity Forecasting,2016
31,United States,TownCentre,oxford_town_centre,42.3354481,-71.16813864,Boston College,edu,869df5e8221129850e81e77d4dc36e6c0f854fe6,citation,https://arxiv.org/pdf/1601.03094.pdf,A metric for sets of trajectories that is practical and mathematically consistent,2016
32,United States,TownCentre,oxford_town_centre,34.1579742,-118.2894729,Disney Research,company,d8bc2e2537cecbe6e751d4791837251a249cd06d,citation,http://www.cse.psu.edu/~rtc12/Papers/wacv2016CarrCollins.pdf,Assessing tracking performance in complex scenarios using mean time between failures,2016
33,United States,TownCentre,oxford_town_centre,40.7982133,-77.8599084,The Pennsylvania State University,edu,d8bc2e2537cecbe6e751d4791837251a249cd06d,citation,http://www.cse.psu.edu/~rtc12/Papers/wacv2016CarrCollins.pdf,Assessing tracking performance in complex scenarios using mean time between failures,2016
34,United States,TownCentre,oxford_town_centre,28.59899755,-81.19712501,University of Central Florida,edu,2dfba157e0b5db5becb99b3c412ac729cf3bb32d,citation,https://pdfs.semanticscholar.org/7fb2/f6ce372db950f26f9395721651d6c6aa7b76.pdf,Automatic Detection and Tracking of Pedestrians in Videos with Various Crowd Densities,2012
35,India,TownCentre,oxford_town_centre,12.9914929,80.2336907,"IIT Madras, India",edu,37f2e03c7cbec9ffc35eac51578e7e8fdfee3d4e,citation,http://www.cse.iitm.ac.in/~amittal/wacv2015_review.pdf,Co-operative Pedestrians Group Tracking in Crowded Scenes Using an MST Approach,2015
36,United Kingdom,TownCentre,oxford_town_centre,55.91029135,-3.32345777,Heriot-Watt University,edu,b8af24279c58a718091817236f878c805a7843e1,citation,https://pdfs.semanticscholar.org/b8af/24279c58a718091817236f878c805a7843e1.pdf,Context Aware Anomalous Behaviour Detection in Crowded Surveillance,2013
37,Russia,TownCentre,oxford_town_centre,55.8067104,37.5416381,"Faculty of Computer Science, Moscow, Russia",edu,224547337e1ace6411a69c2e06ce538bc67923f7,citation,https://pdfs.semanticscholar.org/2245/47337e1ace6411a69c2e06ce538bc67923f7.pdf,Convolutional Neural Network for Camera Pose Estimation from Object Detections,2017
38,Germany,TownCentre,oxford_town_centre,48.7468939,9.0805141,Max Planck Institute for Intelligent Systems,edu,b6d0e461535116a675a0354e7da65b2c1d2958d4,citation,https://arxiv.org/pdf/1805.03430.pdf,Deep Directional Statistics: Pose Estimation with Uncertainty Quantification,2018
39,United States,TownCentre,oxford_town_centre,38.7768106,-94.9442982,Amazon,company,b6d0e461535116a675a0354e7da65b2c1d2958d4,citation,https://arxiv.org/pdf/1805.03430.pdf,Deep Directional Statistics: Pose Estimation with Uncertainty Quantification,2018
40,United States,TownCentre,oxford_town_centre,47.6423318,-122.1369302,Microsoft,company,b6d0e461535116a675a0354e7da65b2c1d2958d4,citation,https://arxiv.org/pdf/1805.03430.pdf,Deep Directional Statistics: Pose Estimation with Uncertainty Quantification,2018
41,United Kingdom,TownCentre,oxford_town_centre,55.91029135,-3.32345777,Heriot-Watt University,edu,70be5432677c0fbe000ac0c28dda351a950e0536,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2014/W14/papers/Leach_Detecting_Social_Groups_2014_CVPR_paper.pdf,Detecting Social Groups in Crowded Surveillance Videos Using Visual Attention,2014
42,Switzerland,TownCentre,oxford_town_centre,47.376313,8.5476699,ETH Zurich,edu,9458642e7645bfd865911140ee8413e2f5f9fcd6,citation,https://pdfs.semanticscholar.org/9458/642e7645bfd865911140ee8413e2f5f9fcd6.pdf,Efficient Multiple People Tracking Using Minimum Cost Arborescences,2014
43,United Kingdom,TownCentre,oxford_town_centre,54.6141723,-5.9002151,Queen's University Belfast,edu,2a7935706d43c01789d43a81a1d391418f220a0a,citation,https://pure.qub.ac.uk/portal/files/31960902/285.pdf,Enhancing Linear Programming with Motion Modeling for Multi-target Tracking,2015
44,Sri Lanka,TownCentre,oxford_town_centre,6.7970862,79.9019094,University of Moratuwa,edu,b183914d0b16647a41f0bfd4af64bf94a83a2b14,citation,http://iwinlab.eng.usf.edu/papers/Extensible%20video%20surveillance%20software%20with%20simultaneous%20event%20detection%20for%20low%20and%20high%20density%20crowd%20analysis.pdf,Extensible video surveillance software with simultaneous event detection for low and high density crowd analysis,2014
45,United States,TownCentre,oxford_town_centre,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,fa5aca45965e312362d2d75a69312a0678fdf5d7,citation,https://pdfs.semanticscholar.org/fa5a/ca45965e312362d2d75a69312a0678fdf5d7.pdf,Fast and Accurate Head Pose Estimation via Random Projection Forests : Supplementary Material,2015
46,United States,TownCentre,oxford_town_centre,37.3641651,-120.4254615,University of California at Merced,edu,fa5aca45965e312362d2d75a69312a0678fdf5d7,citation,https://pdfs.semanticscholar.org/fa5a/ca45965e312362d2d75a69312a0678fdf5d7.pdf,Fast and Accurate Head Pose Estimation via Random Projection Forests : Supplementary Material,2015
47,Australia,TownCentre,oxford_town_centre,-32.8892352,151.6998983,"University of Newcastle, Australia",edu,2feb7c57d51df998aafa6f3017662263a91625b4,citation,https://pdfs.semanticscholar.org/d344/9eaaf392fd07b676e744410049f4095b4b5c.pdf,Feature Selection for Intelligent Transportation Systems,2014
48,Germany,TownCentre,oxford_town_centre,49.01546,8.4257999,Fraunhofer,company,1f82eebadc3ffa41820ad1a0f53770247fc96dcd,citation,https://pdfs.semanticscholar.org/c5ac/81b17b8fcc028f375fbbd090b558ba9a437a.pdf,Using Trajectories derived by Dense Optical Flows as a Spatial Component in Background Subtraction,2016
49,United States,TownCentre,oxford_town_centre,42.3583961,-71.09567788,MIT,edu,b18f94c5296a9cebe9e779d50d193fd180f78ed9,citation,https://arxiv.org/pdf/1604.01431.pdf,Forecasting Interactive Dynamics of Pedestrians with Fictitious Play,2017
50,United Kingdom,TownCentre,oxford_town_centre,51.7520849,-1.2516646,Oxford University,edu,b18f94c5296a9cebe9e779d50d193fd180f78ed9,citation,https://arxiv.org/pdf/1604.01431.pdf,Forecasting Interactive Dynamics of Pedestrians with Fictitious Play,2017
51,United States,TownCentre,oxford_town_centre,37.43131385,-122.16936535,Stanford University,edu,b18f94c5296a9cebe9e779d50d193fd180f78ed9,citation,https://arxiv.org/pdf/1604.01431.pdf,Forecasting Interactive Dynamics of Pedestrians with Fictitious Play,2017
52,Netherlands,TownCentre,oxford_town_centre,52.3553655,4.9501644,University of Amsterdam,edu,687ec23addf5a1279e49cc46b78e3245af94ac7b,citation,https://pdfs.semanticscholar.org/687e/c23addf5a1279e49cc46b78e3245af94ac7b.pdf,UvA-DARE ( Digital Academic Repository ) Visual Tracking : An Experimental Survey Smeulders,2013
53,Italy,TownCentre,oxford_town_centre,45.1847248,9.1582069,"Italian Institute of Technology, Genova, Italy",edu,5ab9f00a707a55f4955b378981ad425aa1cb8ea3,citation,https://arxiv.org/pdf/1901.02000.pdf,Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets,2019
54,Germany,TownCentre,oxford_town_centre,48.1820038,11.5978282,"OSRAM GmbH, Germany",company,5ab9f00a707a55f4955b378981ad425aa1cb8ea3,citation,https://arxiv.org/pdf/1901.02000.pdf,Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets,2019
55,Italy,TownCentre,oxford_town_centre,45.437398,11.003376,University of Verona,edu,5ab9f00a707a55f4955b378981ad425aa1cb8ea3,citation,https://arxiv.org/pdf/1901.02000.pdf,Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets,2019
56,United Kingdom,TownCentre,oxford_town_centre,51.7534538,-1.25400997,University of Oxford,edu,3ed9730e5ec8716e8cdf55f207ef973a9c854574,citation,https://arxiv.org/pdf/1612.05234.pdf,Visual Compiler: Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator,2016
57,United States,TownCentre,oxford_town_centre,29.7207902,-95.34406271,University of Houston,edu,58eba9930b63cc14715368acf40017293b8dc94f,citation,https://pdfs.semanticscholar.org/7508/ac08dd7b9694bcfe71a617df7fcf3df80952.pdf,What Do I See? Modeling Human Visual Perception for Multi-person Tracking,2014
58,United States,TownCentre,oxford_town_centre,29.7207902,-95.34406271,University of Houston,edu,a0b489eeb4f7fd2249da756d829e179a6718d9d1,citation,,"""Seeing is Believing"": Pedestrian Trajectory Forecasting Using Visual Frustum of Attention",2018
59,Belgium,TownCentre,oxford_town_centre,50.8779545,4.7002953,"KULeuven, EAVISE",edu,4ec4392246a7760d189cd6ea48a81664cd2fe4bf,citation,https://pdfs.semanticscholar.org/4ec4/392246a7760d189cd6ea48a81664cd2fe4bf.pdf,GPU Accelerated ACF Detector,2018
60,United States,TownCentre,oxford_town_centre,40.7982133,-77.8599084,The Pennsylvania State University,edu,6e32c368a6157fb911c9363dc3e967a7fb2ad9f7,citation,https://pdfs.semanticscholar.org/8268/d68f6aa510a765466b2c7f2ba2ea34a48c51.pdf,Hybrid Stochastic / Deterministic Optimization for Tracking Sports Players and Pedestrians,2014
61,United States,TownCentre,oxford_town_centre,40.4439789,-79.9464634,Disney Research Pittsburgh,edu,6e32c368a6157fb911c9363dc3e967a7fb2ad9f7,citation,https://pdfs.semanticscholar.org/8268/d68f6aa510a765466b2c7f2ba2ea34a48c51.pdf,Hybrid Stochastic / Deterministic Optimization for Tracking Sports Players and Pedestrians,2014
62,India,TownCentre,oxford_town_centre,13.0304619,77.5646862,"M.S. Ramaiah Institute of Technology, Bangalore, India",edu,6f089f9959cc711e16f1ebe0c6251aaf8a65959a,citation,https://pdfs.semanticscholar.org/6f08/9f9959cc711e16f1ebe0c6251aaf8a65959a.pdf,Improvement in object detection using Super Pixels,2016
63,United States,TownCentre,oxford_town_centre,38.99203005,-76.9461029,University of Maryland College Park,edu,4e82908e6482d973c280deb79c254631a60f1631,citation,https://pdfs.semanticscholar.org/4e82/908e6482d973c280deb79c254631a60f1631.pdf,Improving Efficiency and Scalability in Visual Surveillance Applications,2013
64,United States,TownCentre,oxford_town_centre,37.8718992,-122.2585399,University of California,edu,38b5a83f7941fea5fd82466f8ce1ce4ed7749f59,citation,http://rlair.cs.ucr.edu/papers/docs/grouptracking.pdf,Improving multi-target tracking via social grouping,2012
65,Singapore,TownCentre,oxford_town_centre,1.3484104,103.68297965,Nanyang Technological University,edu,13caf4d2e0a4b6fcfcd4b9e8e2341b8ebd38258d,citation,https://arxiv.org/pdf/1605.04502.pdf,Joint Learning of Siamese CNNs and Temporally Constrained Metrics for Tracklet Association,2016
66,United States,TownCentre,oxford_town_centre,35.9049122,-79.0469134,The University of North Carolina at Chapel Hill,edu,45e459462a80af03e1bb51a178648c10c4250925,citation,https://arxiv.org/pdf/1606.08998.pdf,LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning,2016
67,China,TownCentre,oxford_town_centre,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,c0262e24324a6a4e6af5bd99fc79e2eb802519b3,citation,https://arxiv.org/pdf/1611.03968.pdf,Learning Scene-specific Object Detectors Based on a Generative-Discriminative Model with Minimal Supervision,2016
68,China,TownCentre,oxford_town_centre,30.527151,114.400762,China University of Geosciences,edu,c0262e24324a6a4e6af5bd99fc79e2eb802519b3,citation,https://arxiv.org/pdf/1611.03968.pdf,Learning Scene-specific Object Detectors Based on a Generative-Discriminative Model with Minimal Supervision,2016
69,China,TownCentre,oxford_town_centre,32.0565957,118.77408833,Nanjing University,edu,c0262e24324a6a4e6af5bd99fc79e2eb802519b3,citation,https://arxiv.org/pdf/1611.03968.pdf,Learning Scene-specific Object Detectors Based on a Generative-Discriminative Model with Minimal Supervision,2016
70,United Kingdom,TownCentre,oxford_town_centre,51.5247272,-0.03931035,Queen Mary University of London,edu,1883387726897d94b663cc4de4df88e5c31df285,citation,http://www.eecs.qmul.ac.uk/~andrea/papers/2014_TIP_MultiTargetTrackingEvaluation_Tahir_Poiesi_Cavallaro.pdf,Measures of Effective Video Tracking,2014
71,United States,TownCentre,oxford_town_centre,35.9113971,-79.0504529,University of North Carolina at Chapel Hill,edu,8d2bf6ecbfda94f57000b84509bf77f4c47c1c66,citation,https://arxiv.org/pdf/1707.09100.pdf,MixedPeds: Pedestrian Detection in Unannotated Videos Using Synthetically Generated Human-Agents for Training,2018
72,United States,TownCentre,oxford_town_centre,37.8718992,-122.2585399,University of California,edu,b506aa23949b6d1f0c868ad03aaaeb5e5f7f6b57,citation,http://rlair.cs.ucr.edu/papers/docs/zqin-phd.pdf,Modeling Social and Temporal Context for Video Analysis,2015
73,Australia,TownCentre,oxford_town_centre,-34.920603,138.6062277,Adelaide University,edu,5bae9822d703c585a61575dced83fa2f4dea1c6d,citation,https://arxiv.org/pdf/1504.01942.pdf,MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking,2015
74,Switzerland,TownCentre,oxford_town_centre,47.376313,8.5476699,ETH Zurich,edu,5bae9822d703c585a61575dced83fa2f4dea1c6d,citation,https://arxiv.org/pdf/1504.01942.pdf,MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking,2015
75,Germany,TownCentre,oxford_town_centre,49.8748277,8.6563281,TU Darmstadt,edu,5bae9822d703c585a61575dced83fa2f4dea1c6d,citation,https://arxiv.org/pdf/1504.01942.pdf,MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking,2015
76,United States,TownCentre,oxford_town_centre,37.8718992,-122.2585399,University of California,edu,e6d48d23308a9e0a215f7b5ba6ae30ee5d2f0ef5,citation,https://pdfs.semanticscholar.org/e6d4/8d23308a9e0a215f7b5ba6ae30ee5d2f0ef5.pdf,Multi-person Tracking by Online Learned Grouping Model with Non-linear Motion Context,2015
77,France,TownCentre,oxford_town_centre,45.217886,5.807369,INRIA,edu,fc30d7dbf4c3cdd377d8cd4e7eeabd5d73814b8f,citation,https://pdfs.semanticscholar.org/fc30/d7dbf4c3cdd377d8cd4e7eeabd5d73814b8f.pdf,Multiple Object Tracking by Efficient Graph Partitioning,2014
78,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,290eda31bc13cbd5933acec8b6a25b3e3761c788,citation,https://arxiv.org/pdf/1411.7935.pdf,Multiple object tracking with context awareness,2014
79,Czech Republic,TownCentre,oxford_town_centre,49.20172,16.6033168,Brno University of Technology,edu,dc53c4bb04e787a0d45dd761ba2101cc51c17b82,citation,https://pdfs.semanticscholar.org/dc53/c4bb04e787a0d45dd761ba2101cc51c17b82.pdf,Multiple-Person Tracking by Detection,2016
80,Germany,TownCentre,oxford_town_centre,48.1820038,11.5978282,"OSRAM GmbH, Germany",company,943b1b92b5bdee0b5770418c645a4a17bded1ccf,citation,https://arxiv.org/pdf/1805.00652.pdf,MX-LSTM: Mixing Tracklets and Vislets to Jointly Forecast Trajectories and Head Poses,2018
81,Italy,TownCentre,oxford_town_centre,45.437398,11.003376,University of Verona,edu,943b1b92b5bdee0b5770418c645a4a17bded1ccf,citation,https://arxiv.org/pdf/1805.00652.pdf,MX-LSTM: Mixing Tracklets and Vislets to Jointly Forecast Trajectories and Head Poses,2018
82,France,TownCentre,oxford_town_centre,48.8422058,2.3451689,"INRIA / Ecole Normale Supérieure, France",edu,47119c99f5aa1e47bbeb86de0f955e7c500e6a93,citation,https://arxiv.org/pdf/1408.3304.pdf,On pairwise costs for network flow multi-object tracking,2015
83,United States,TownCentre,oxford_town_centre,42.3504253,-71.10056114,Boston University,edu,1ae3dd081b93c46cda4d72100d8b1d59eb585157,citation,https://pdfs.semanticscholar.org/fea1/0f39b0a77035fb549fc580fd951384b79f9b.pdf,Online Motion Agreement Tracking,2013
84,Malaysia,TownCentre,oxford_town_centre,4.3400673,101.1429799,Universiti Tunku Abdul Rahman,edu,e1f815c50a6c0c6d790c60a1348393264f829e60,citation,https://pdfs.semanticscholar.org/e1f8/15c50a6c0c6d790c60a1348393264f829e60.pdf,PEDESTRIAN DETECTION AND TRACKING IN SURVEILLANCE VIDEO By PENNY CHONG,2016
85,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,422d352a7d26fef692a3cd24466bfb5b4526efea,citation,https://pdfs.semanticscholar.org/422d/352a7d26fef692a3cd24466bfb5b4526efea.pdf,Pedestrian interaction in tracking : the social force model and global optimization methods,2012
86,Sweden,TownCentre,oxford_town_centre,57.6897063,11.9741654,Chalmers University of Technology,edu,367b5b814aa991329c2ae7f8793909ad8c0a56f1,citation,https://arxiv.org/pdf/1211.0191.pdf,Performance evaluation of random set based pedestrian tracking algorithms,2013
87,Japan,TownCentre,oxford_town_centre,35.5152072,134.1733553,Tottori University,edu,9d89f1bc88fd65e90b31a2129719384796bed17a,citation,http://vision.unipv.it/CV/materiale2016-17/2nd%20Choice/0225.pdf,Person re-identification using co-occurrence attributes of physical and adhered human characteristics,2016
88,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,48705017d91a157949cfaaeb19b826014899a36b,citation,https://pdfs.semanticscholar.org/4870/5017d91a157949cfaaeb19b826014899a36b.pdf,PROBABILISTIC MULTI-PERSON TRACKING USING DYNAMIC BAYES NETWORKS,2015
89,Italy,TownCentre,oxford_town_centre,39.2173657,9.1149218,"Università degli Studi di Cagliari, Italy",edu,7c1f47ca50a8a55f93bf69791d9df2f994019758,citation,http://veprints.unica.it/1295/1/PhD_ThesisPalaF.pdf,Re-identification and semantic retrieval of pedestrians in video surveillance scenarios,2016
90,United Kingdom,TownCentre,oxford_town_centre,51.5247272,-0.03931035,Queen Mary University of London,edu,3a28059df29b74775f77fd20a15dc6b5fe857556,citation,https://pdfs.semanticscholar.org/3a28/059df29b74775f77fd20a15dc6b5fe857556.pdf,Riccardo Mazzon PhD Thesis 2013,2013
91,Brazil,TownCentre,oxford_town_centre,-30.0338248,-51.218828,Federal University of Rio Grande do Sul,edu,057517452369751bd63d83902ea91558d58161da,citation,http://inf.ufrgs.br/~gfuhr/papers/102095_3.pdf,Robust Patch-Based Pedestrian Tracking Using Monocular Calibrated Cameras,2012
92,China,TownCentre,oxford_town_centre,28.727339,115.816633,Jiangxi University of Finance and Economics,edu,1642358cd9410abe9ee512d34ba68296b308770e,citation,https://arxiv.org/pdf/1807.04562.pdf,Robustness Analysis of Pedestrian Detectors for Surveillance,2018
93,Singapore,TownCentre,oxford_town_centre,1.3484104,103.68297965,Nanyang Technological University,edu,1642358cd9410abe9ee512d34ba68296b308770e,citation,https://arxiv.org/pdf/1807.04562.pdf,Robustness Analysis of Pedestrian Detectors for Surveillance,2018
94,China,TownCentre,oxford_town_centre,34.250803,108.983693,Xi’an Jiaotong University,edu,1642358cd9410abe9ee512d34ba68296b308770e,citation,https://arxiv.org/pdf/1807.04562.pdf,Robustness Analysis of Pedestrian Detectors for Surveillance,2018
95,Singapore,TownCentre,oxford_town_centre,1.3484104,103.68297965,Nanyang Technological University,edu,7c132e0a2b7e13c78784287af38ad74378da31e5,citation,https://pdfs.semanticscholar.org/7c13/2e0a2b7e13c78784287af38ad74378da31e5.pdf,Salient Parts based Multi-people Tracking,2015
96,China,TownCentre,oxford_town_centre,40.0044795,116.370238,Chinese Academy of Sciences,edu,679136c2844eeddca34e98e483aca1ff6ef5e902,citation,https://arxiv.org/pdf/1712.08745.pdf,Scene-Specific Pedestrian Detection Based on Parallel Vision,2017
97,China,TownCentre,oxford_town_centre,34.250803,108.983693,Xi’an Jiaotong University,edu,679136c2844eeddca34e98e483aca1ff6ef5e902,citation,https://arxiv.org/pdf/1712.08745.pdf,Scene-Specific Pedestrian Detection Based on Parallel Vision,2017
98,China,TownCentre,oxford_town_centre,40.0044795,116.370238,Chinese Academy of Sciences,edu,57e9b0d3ab6295e914d5a30cfaa3b2c81189abc1,citation,https://arxiv.org/pdf/1611.07544.pdf,Self-Learning Scene-Specific Pedestrian Detectors Using a Progressive Latent Model,2017
99,United States,TownCentre,oxford_town_centre,35.9990522,-78.9290629,Duke University,edu,57e9b0d3ab6295e914d5a30cfaa3b2c81189abc1,citation,https://arxiv.org/pdf/1611.07544.pdf,Self-Learning Scene-Specific Pedestrian Detectors Using a Progressive Latent Model,2017
100,Switzerland,TownCentre,oxford_town_centre,47.3764534,8.54770931,ETH Zürich,edu,70b42bbd76e6312d39ea06b8a0c24beb4a93e022,citation,http://www.tnt.uni-hannover.de/papers/data/1075/WACV2015_Abstract.pdf,Solving Multiple People Tracking in a Minimum Cost Arborescence,2015
101,United States,TownCentre,oxford_town_centre,42.718568,-84.47791571,Michigan State University,edu,acf0db156406ddad1ace2ff2696cb60d0a04cf7c,citation,http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf,Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,2018
102,United Kingdom,TownCentre,oxford_town_centre,51.7534538,-1.25400997,University of Oxford,edu,acf0db156406ddad1ace2ff2696cb60d0a04cf7c,citation,http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf,Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,2018
103,Japan,TownCentre,oxford_town_centre,36.05238585,140.11852361,Institute of Industrial Science,edu,acf0db156406ddad1ace2ff2696cb60d0a04cf7c,citation,http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf,Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,2018
104,United States,TownCentre,oxford_town_centre,40.4441619,-79.94272826,Carnegie Mellon University,edu,acf0db156406ddad1ace2ff2696cb60d0a04cf7c,citation,http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf,Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,2018
105,Sweden,TownCentre,oxford_town_centre,57.7172004,11.9218558,"Volvo Construction Equipment, Göthenburg, Sweden",company,acf0db156406ddad1ace2ff2696cb60d0a04cf7c,citation,http://hal.cse.msu.edu/assets/pdfs/papers/2018-ijcv-visual-compiler.pdf,Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,2018
106,United States,TownCentre,oxford_town_centre,35.9990522,-78.9290629,Duke University,edu,64e0690dd176a93de9d4328f6e31fc4afe1e7536,citation,https://pdfs.semanticscholar.org/64e0/690dd176a93de9d4328f6e31fc4afe1e7536.pdf,Tracking Multiple People Online and in Real Time,2014
107,Switzerland,TownCentre,oxford_town_centre,47.3764534,8.54770931,ETH Zürich,edu,64c78c8bf779a27e819fd9d5dba91247ab5a902b,citation,https://arxiv.org/pdf/1607.07304.pdf,Tracking with multi-level features.,2016
108,Germany,TownCentre,oxford_town_centre,52.381515,9.720171,Leibniz Universität Hannover,edu,64c78c8bf779a27e819fd9d5dba91247ab5a902b,citation,https://arxiv.org/pdf/1607.07304.pdf,Tracking with multi-level features.,2016
109,Germany,TownCentre,oxford_town_centre,48.14955455,11.56775314,Technical University Munich,edu,64c78c8bf779a27e819fd9d5dba91247ab5a902b,citation,https://arxiv.org/pdf/1607.07304.pdf,Tracking with multi-level features.,2016
110,Singapore,TownCentre,oxford_town_centre,1.3484104,103.68297965,Nanyang Technological University,edu,7d3698c0e828d05f147682b0f5bfcd3b681ff205,citation,https://arxiv.org/pdf/1511.06654.pdf,Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation,2017
111,Australia,TownCentre,oxford_town_centre,-35.2809368,149.1300092,"NICTA, Canberra",edu,f0cc615b14c97482faa9c47eb855303c71ff03a7,citation,https://pdfs.semanticscholar.org/f0cc/615b14c97482faa9c47eb855303c71ff03a7.pdf,Tracklet clustering for robust multiple object tracking using distance dependent Chinese restaurant processes,2016
112,Germany,TownCentre,oxford_town_centre,52.5180641,13.3250425,TU Berlin,edu,c4cd19cf41a2f5cd543d81b94afe6cc42785920a,citation,http://elvera.nue.tu-berlin.de/files/1491Bochinski2016.pdf,Training a convolutional neural network for multi-class object detection using solely virtual world data,2016
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