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Standardized benchmarks are crucial for the majority of computer vision applications.
Multi-target tracking using joint probabilistic data association
T. E. Fortmann, Y. Bar-Shalom, and M. Scheffe · 1980
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A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
D. Scharstein and R. Szeliski · 2002
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A comparison and evaluation of multi-view stereo reconstruction algorithms
S. M. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski · 2006
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The clear 2006 evaluation
R. Stiefelhagen, K. Bernardin, R. Bowers, J. S. Garofolo, D. Mostefa, and P. Soundararajan · 2006
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Tracking of multiple, partially occluded humans based on static body part detection
B. Wu and R. Nevatia · 2006
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Labeled faces in the wild: A database for studying face recognition in unconstrained environments
G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller · 2007
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Evaluating multiple object tracking performance: The CLEAR MOT metrics
K. Bernardin and R. Stiefelhagen · 2008
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A mobile vision system for robust multi-person tracking
A. Ess, B. Leibe, K. Schindler, and L. Van Gool · 2008
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A consistent metric for performance evaluation of multi-object filters
D. Schuhmacher, B.-T. Vo, and B.-N. Vo · 2008
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Global data association for multi-object tracking using network flows
L. Zhang, Y. Li, and R. Nevatia · 2008
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Pedestrian detection: A benchmark
P. Dollár, C. Wojek, B. Schiele, and P. Perona · 2009
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Framework for performance evaluation for face, text and vehicle detection and tracking in video: data, metrics, and protocol
R. Kasturi, D. Goldgof, P. Soundararajan, V. Manohar, J. Garofolo, M. Boonstra, V. Korzhova, and J. Zhang · 2009
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Learning to associate: Hybridboosted multi-target tracker for crowded scene
Y. Li, C. Huang, and R. Nevatia · 2009
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Object detection with discriminatively trained part based models
P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan · 2010
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PETS2010: Dataset and challenge
J. Ferryman and A. Ellis · 2010
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Multi-target tracking by continuous energy minimization
A. Andriyenko and K. Schindler · 2011
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A database and evaluation methodology for optical flow
S. Baker, D. Scharstein, J. P. Lewis, S. Roth, M. J. Black, and R. Szeliski · 2011
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Globally optimal solution to multi-object tracking with merged measurements
J. a. Henriques, R. Caseiro, and J. Batista · 2011
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Globally-optimal greedy algorithms for tracking a variable number of objects
H. Pirsiavash, D. Ramanan, and C. C. Fowlkes · 2011
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Unbiased look at dataset bias
A. Torralba and A. Efros · 2011
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The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
M. Everingham, L. Van Gool, C. Williams, J. Winn, and A. Zisserman · 2012
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Are we ready for autonomous driving? The KITTI Vision Benchmark Suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning
S.-H. Bae and K.-J. Yoon · 2014
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Fast feature pyramids for object detection
P. Dollár, R. Appel, S. Belongie, and P. Perona · 2014
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3d traffic scene understanding from movable platforms
A. Geiger, M. Lauer, C. Wojek, C. Stiller, and R. Urtasun · 2014
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The visual object tracking VOT2014 challenge results
M. Kristan et al · 2014
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Lerning an image-based motion context for multiple people tracking
L. Leal-Taixé, M. Fenzi, A. Kuznetsova, B. Rosenhahn, and S. Savarese · 2014
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Face detection without bells and whistles
M. Mathias, R. Benenson, M. Pedersoli, and L. V. Gool · 2014
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Cited alongside, same era.
Branch-and-price global optimization for multi-view multi-object tracking
L. Leal-Taixé, G. Pons-Moll, and B. Rosenhahn · 2012
Cited alongside, same era.
GMCP-Tracker: Global multi-object tracking using generalized minimum clique graphs
A. R. Zamir, A. Dehghan, and M. Shah · 2012
Cited alongside, same era.
Multi-target tracking by Lagrangian relaxation to min-cost network flow
A. A. Butt and R. T. Collins · 2013
Cited alongside, same era.
The way they move: Tracking multiple targets with similar appearance
C. Dicle, M. Sznaier, and O. Camps · 2013
Cited alongside, same era.
Tracking sports players with context-conditioned motion models
J. Liu, P. Carr, R. T. Collins, and Y. Liu · 2013
Cited alongside, same era.
Challenges of ground truth evaluation of multi-target tracking
A. Milan, K. Schindler, and S. Roth · 2013
Cited alongside, same era.
Continuous energy minimization for multitarget tracking
A. Milan, S. Roth, and K. Schindler · 2014
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Tracklet association with online target-specific metric learning
B. Wang, G. Wang, K. L. Chan, and L. Wang · 2014
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Multiple target tracking based on undirected hierarchical relation hypergraph
L. Wen, W. Li, J. Yan, Z. Lei, D. Yi, and S. Z. Li · 2014
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http://www.igp.ethz.ch/photogrammetry/bmtt2015/home.html
1st Workshop on Benchmarking Multi-Target Tracking, 2015 · 2015
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Fast R-CNN
R. Girshick · 2015
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Deformable part models are convolutional neural networks
R. Girshick, F. Iandola, T. Darrell, and J. Malik · 2015
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MOTChallenge 2015: Towards a benchmark for multi-target tracking
L. Leal-Taixé, A. Milan, I. Reid, S. Roth, and K. Schindler · 2015
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Joint probabilistic data association revisited
H. S. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, and I. Reid · 2015
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Detrac: A new benchmark and protocol for multi-object tracking
L. Wen, D. Du, Z. Cai, Z. Lei, M.-C. Chang, H. Qi, J. Lim, M.-H. Yang, and S. Lyu · 2015
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