多目标跟踪 近年论文及开源代码汇总

作者 |  ZihaoZhao

来源 | https://zhuanlan.zhihu.com/p/65177442

把最近几年的MOT论文和开源代码按时间顺序整理了一下，对14年之后的论文整理的比较详细，14年之前的比较简略，希望对大家有帮助。

论文的Short Name前带  ✔  的论文有代码，代码链接在论文链接之后。

这篇文章之后会持续更新最新的论文和代码。

另，MOT综述较少，Overview里也会列一些相关领域的综述。

Overview

Emami, P., Pardalos, P. M., Elefteriadou, L., & Ranka, S. (2018). Machine Learning Methods for Solving Assignment Problems in Multi-Target Tracking, 1(1), 1–35. Retrieved from  arxiv.org/abs/1802.06897

Leal-Taixé, L., Milan, A., Schindler, K., Cremers, D., Reid, I., & Roth, S. (2017). Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking, (March). Retrieved from  arxiv.org/abs/1704.0278

Luo, W., Xing, J., Milan, A., Zhang, X., Liu, W., Zhao, X., & Kim, T.-K. (2014). Multiple Object Tracking: A Literature Review, 1–18. Retrieved from arxiv.org/abs/1409.7618

Li, X., Hu, W., Shen, C., Zhang, Z., & Dick, A. (2013). A Survey of Appearance Models in Visual Object Tracking, 1–42.from  arxiv.org/pdf/1303.4803

Poore, A. B., & Gadaleta, S. (2006). Some assignment problems arising from multiple target tracking, 43, 1074–1091. from  doi.org/10.1016/j.mcm.2

Yilmaz, A., & Javed, O. (2006). Object Tracking : A Survey, 38(4). from  doi.org/10.1145/1177352

2019

✔FANTrack Baser, E., Balasubramanian, V., Bhattacharyya, P., & Czarnecki, K. (2019). FANTrack: 3D Multi-Object Tracking with Feature Association Network. Retrieved from  https://arxiv.org/abs/1905.02843    https://git.uwaterloo.ca/wise-lab/fantrack

FMA Zhang, J., Zhou, S., Wang, J., & Huang, D. (2019). Frame-wise Motion and Appearance for Real-time Multiple Object Tracking, (1). Retrieved from  arxiv.org/abs/1905.02292

FAMNet Chu, P., & Ling, H. (2019). FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking. Retrieved from  arxiv.org/abs/1904.04989

STRN Xu, J., Cao, Y., Zhang, Z., & Hu, H. (2019). Spatial-Temporal Relation Networks for Multi-Object Tracking. Retrieved from  arxiv.org/abs/1904.11489

IATracker Chu, P., Fan, H., Tan, C. C., & Ling, H. (2019). Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment. Retrieved from  arxiv.org/abs/1902.08231

LSST Feng, W., Hu, Z., Wu, W., Yan, J., & Ouyang, W. (2019). Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification. LSST Retrieved from  arxiv.org/abs/1901.06129

✔NT Longyin Wen , Dawei Du , Shengkun Li, Xiao Bian, Siwei Lyu Learning Non-Uniform Hypergraph for Multi-Object Tracking, In AAAI 2019 from  http://www.cs.albany.edu/~lsw/papers/aaai19a.pdf  from  github.com/longyin880815

MOTS Voigtlaender, P., Krause, M., Osep, A., Luiten, J., Sekar, B. B. G., Geiger, A., & Leibe, B. (2019). MOTS: Multi-Object Tracking and Segmentation. Retrieved from  arxiv.org/abs/1902.03604

2018

DeepCC Ristani, E., & Tomasi, C. (2018). Features for Multi-Target Multi-Camera Tracking and Re-Identification. from  ieeexplore.ieee.org/document/8578730

SADF 48.3@17 Yoon, Y., Boragule, A., Song, Y., Yoon, K., & Jeon, M. (2018). Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering. from  ieeexplore.ieee.org/document/8639078

✔DAN(SST) Sun, S., Akhtar, N., Song, H., Mian, A., & Shah, M. (2018). Deep Affinity Network for Multiple Object Tracking,  13 (9), 1–15. Retrieved from  arxiv.org/abs/1810.11780  from  github.com/shijieS/SST

DMAN Zhu, J., Yang, H., Liu, N., Kim, M., Zhang, W., & Yang, M. H. (2018). Online Multi-Object Tracking with Dual Matching Attention Networks.  Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ,  11209 LNCS , 379–396. from  doi.org/10.1007/978-3-030-01228-1_23

TNT(TrackletNet Tracker) Wang, G., Wang, Y., Zhang, H., Gu, R., & Hwang, J.-N. (2018). Exploit the Connectivity: Multi-Object Tracking with TrackletNet. Retrieved from  arxiv.org/abs/1811.07258

CCC Keuper, M., Tang, S., Andres, B., Brox, T., & Schiele, B. (2018). Motion Segmentation & Multiple Object Tracking by Correlation Co-Clustering.  IEEE Transactions on Pattern Analysis and Machine Intelligence ,  8828 (c), 1–13. from  doi.org/10.1109/TPAMI.2018.2876253

HAF Sheng, H., Zhang, Y., Chen, J., Xiong, Z., & Zhang, J. (2018). Heterogeneous Association Graph Fusion for Target Association in Multiple Object Tracking.  IEEE Transactions on Circuits and Systems for Video Technology ,  XX (X). from  doi.org/10.1109/TCSVT.2018.2882192

TAT(Tracklet Association Tracker) Shen, H., Huang, L., Huang, C., & Xu, W. (2018). Tracklet Association Tracker: An End-to-End Learning-based Association Approach for Multi-Object Tracking. Retrieved from  arxiv.org/abs/1808.01562

Henschel, R., Leal-Taixe, L., Cremers, D., & Rosenhahn, B. (2018). Fusion of head and full-body detectors for multi-object tracking.  IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops ,  2018 – June , 1509–1518. from  doi.org/10.1109/CVPRW.2018.00192

✔MOTBeyondPixels Sharma, S., Ansari, J. A., Murthy, J. K., & Krishna, K. M. (2018). Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking. Retrieved from  arxiv.org/abs/1802.09298  from  github.com/JunaidCS032/MOTBeyondPixels

✔MOTDT Long Chen, Haizhou Ai, Zijie Zhuang, Chong Shang, Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification, ICME 2018 from  arxiv.org/abs/1809.04427  from  github.com/longcw/MOTDT

✔DetTA Breuers, S., Beyer, L., Rafi, U., & Leibe, B. (2018). Detection-Tracking for Efficient Person Analysis: The DetTA Pipeline. Retrieved from  arxiv.org/abs/1804.10134  from  github.com/sbreuers/detta

C-DRL Ren, L., Lu, J., Wang, Z., Tian, Q., & Zhou, J. (n.d.). Collaborative Deep Reinforcement Learning for Multi-Object Tracking, 1–17. from  openaccess.thecvf.com/content_ECCV_2018/papers/Liangliang_Ren_Collaborative_Deep_Reinforcement_ECCV_2018_paper.pdf

MHT-bLSTM Kim, C., Li, F., & Rehg, J. M. (n.d.). Multi-object Tracking with Neural Gating Using Bilinear LSTM. from  openaccess.thecvf.com/content_ECCV_2018/papers/Chanho_Kim_Multi-object_Tracking_with_ECCV_2018_paper.pdf

THOPA-net Fabbri, M., Lanzi, F., Calderara, S., & Vezzani, R. (2018). Learning to Detect and Track Visible and Occluded Body Joints in a Virtual World, (April). from  researchgate.net/publication/323957071_Learning_to_Detect_and_Track_Visible_and_Occluded_Body_Joints_in_a_Virtual_World

PHD Fang, K., Xiang, Y., Li, X., & Savarese, S. (2018). Recurrent Autoregressive Networks for Online Multi-Object Tracking.  WACV . from  yuxng.github.io/fang_wacv18.pdf

Ma, C., Yang, C., Yang, F., Zhuang, Y., Zhang, Z., Jia, H., & Xie, X. (2018). Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking. Retrieved from  arxiv.org/abs/1804.04555

Fernando, T., Denman, S., Sridharan, S., & Fookes, C. (2018). Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking. Retrieved from  arxiv.org/abs/1803.03347

2017

DeepNetworkFlows Schulter, S., Vernaza, P., Choi, W., & Chandraker, M. (2017). Deep network flow for multi-object tracking.  Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 ,  2017 – Janua , 2730–2739. from  doi.org/10.1109/CVPR.2017.292

✔DeepSORT Wojke, N., Bewley, A., & Paulus, D. (2017). Simple Online and Realtime Tracking with a Deep Association Metric.  Proceedings - International Conference on Image Processing, ICIP ,  2017 – Septe , 3645–3649. from  doi.org/10.1109/ICIP.2017.8296962  from  github.com/nwojke/deep_sort

EAMTT Tang, S., Andriluka, M., Andres, B., & Schiele, B. (2017). Multiple people tracking by lifted multicut and person re-identification.  Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 ,  2017 – Janua , 3701–3710. from doi.org/10.1109/CVPR.2017.394

SOTforMOT He, Q., Wu, J., Yu, G., & Zhang, C. (2017). SOT for MOT. Retrieved from  arxiv.org/abs/1712.01059

✔NMGC-MOT Maksai, A., Wang, X., Fleuret, F., & Fua, P. (2017). Non-Markovian Globally Consistent Multi-Object Tracking.  Iccv 2017 , 2544–2554. Retrieved from  openaccess.thecvf.com/content_ICCV_2017/papers/Maksai_Non-Markovian_Globally_Consistent_ICCV_2017_paper.pdf   from   github.com/maksay/ptrack_cpp

STAM(spatial- temporal attention mechanism) Chu, Q., Ouyang, W., Li, H., Wang, X., Liu, B., & Yu, N. (2017). Online Multi-object Tracking Using CNN-Based Single Object Tracker with Spatial-Temporal Attention Mechanism.  Proceedings of the IEEE International Conference on Computer Vision ,  2017 – Octob , 4846–4855. from  doi.org/10.1109/ICCV.2017.518

Sadeghian, A., Alahi, A., & Savarese, S. (2017). Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies.  Proceedings of the IEEE International Conference on Computer Vision ,  2017 – Octob , 300–311. from  doi.org/10.1109/ICCV.2017.41

Quad-CNN Son, J., Baek, M., Cho, M., & Han, B. (2017). Multi-object tracking with quadruplet convolutional neural networks.  Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 ,  2017 – Janua , 3786–3795. from  doi.org/10.1109/CVPR.2017.403

✔IOUTracker Bochinski, E., Eiselein, V., & Sikora, T. (2017). High-Speed tracking-by-detection without using image information.  2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017 , (August). from  doi.org/10.1109/AVSS.2017.8078516  from  github.com/bochinski/iou-tracker/

✔RNN_LSTM Milan, A., Rezatofighi, S. H., Dick, A., Reid, I., & Schindler, K. (2017). Online Multi-Target Tracking Using Recurrent Neural Networks. AAAI 2017 from  arxiv.org/abs/1604.03635  from  bitbucket.org/amilan/rnntracking

✔D2T Feichtenhofer, C., Pinz, A., & Zisserman, A. (2017). Detect to Track and Track to Detect.  Proceedings of the IEEE International Conference on Computer Vision ,  2017 – Octob , 3057–3065. from  doi.org/10.1109/ICCV.2017.330  from  github.com/feichtenhofer/Detect-Track

✔RCMSS Naiel, M. A., Ahmad, M. O., Swamy, M. N. S., Lim, J., & Yang, M. H. (2017). Online multi-object tracking via robust collaborative model and sample selection.  Computer Vision and Image Understanding ,  154 , 94–107. from  doi.org/10.1016/j.cviu.2016.07.003  from  users.encs.concordia.ca/~rcmss/

✔towards-reid-tracking Beyer, L., Breuers, S., Kurin, V., & Leibe, B. (2017). Towards a Principled Integration of Multi-Camera Re-Identification and Tracking through Optimal Bayes Filters. Retrieved from  arxiv.org/abs/1705.04608  from  github.com/VisualComputingInstitute/towards-reid-tracking

✔CIWT Aljoˇsa Oˇsep, Alexander Hermans Combined Image and World-Space Tracking in Traffic Scenes In ICRA 2017 from  vision.rwth-aachen.de/media/papers/paper_final_compressed.pdf  from  github.com/aljosaosep/ciwt

2016

MTMCT Ristani, E., Solera, F., Zou, R. S., Cucchiara, R., & Tomasi, C. (2016). Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking.  Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ,  9914 LNCS (c), 17–35. from  doi.org/10.1007/978-3-319-48881-3_2

CPD(Changing Point Detection) Lee, B., Erdenee, E., Jin, S., & Rhee, P. K. (2016). Multi-Class Multi-Object Tracking using Changing Point Detection, (Mcmc). from  doi.org/10.1007/978-3-319-48881-3

POI Yu, F., Li, W., Li, Q., Liu, Y., Shi, X., & Yan, J. (2016). POI: Multiple Object Tracking with High Performance Detection and Appearance Feature.  Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ,  9914 LNCS , 36–42. from  doi.org/10.1007/978-3-319-48881-3_3

Social-LSTM Goel, K., Fei-Fei, L., Savarese, S., Alahi, A., Robicquet, A., & Ramanathan, V. (2016). Social LSTM: Human Trajectory Prediction in Crowded Spaces.  2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 961–971. from  doi.org/10.1109/cvpr.2016.110

MOT16 Milan, A., Leal-Taixe, L., Reid, I., Roth, S., & Schindler, K. (2016). MOT16: A Benchmark for Multi-Object Tracking, 1–12. Retrieved from  arxiv.org/abs/1603.00831

✔SORT Bewley, A., Ge, Z., Ott, L., Ramos, F., & Upcroft, B. (2016). Simple online and realtime tracking.  Proceedings - International Conference on Image Processing, ICIP ,  2016 – Augus , 3464–3468. from  doi.org/10.1109/ICIP.2016.7533003  from  github.com/abewley/sort

ArtTrack Insafutdinov, E., Andriluka, M., Pishchulin, L., Tang, S., Levinkov, E., Andres, B., & Schiele, B. (2016). ArtTrack: Articulated Multi-person Tracking in the Wild, 1–12. Retrieved from  arxiv.org/abs/1612.01465

2015

Fagot-bouquet, L., Audigier, R., Dhome, Y., & Multi-person, F. L. O. (2018). Online Multi-person Tracking Based on Global Sparse Collaborative Representations, ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7328364 from  https://ieeexplore.ieee.org/document/7351235

Behavior-CNN Rohrbach, A., Rohrbach, M., Hu, R., Darrell, T., & Schiele, B. (2015). Pedestrian Behavior Understanding and Prediction with Deep Neural Networks.  1511.03745V1 ,  9905 (c), 1–10. from  doi.org/10.1007/978-3-319-46448-0_49

MOT15 Leal-Taixé, L., Milan, A., Reid, I., Roth, S., & Schindler, K. (2015). MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking, 1–15. Retrieved from  arxiv.org/abs/1504.01942

JPDArevisited Rezatofighi, S. H., Milan, A., Zhang, Z., Shi, Q., Dick, A., & Reid, I. (2015). Modified Joint Probabilistic Data Association.  IEEE International Conference on Computer Vision (ICCV) , (December), 6615–6620. from  doi.org/10.1109/ICCV.2015.349

ALFD Choi, W. (2015). Near-online multi-target tracking with aggregated local flow descriptor.  Proceedings of the IEEE International Conference on Computer Vision ,  2015 Inter , 3029–3037. from  doi.org/10.1109/ICCV.2015.347

✔MDP Xiang, Y., Alahi, A., & Savarese, S. (2015). Learning to Track: Online Multi-object Tracking by Decision Making. In  2015 IEEE International Conference on Computer Vision (ICCV) (pp. 4705–4713). IEEE. from  doi.org/10.1109/ICCV.2015.534  from  cvgl.stanford.edu/projects/MDP_tracking/

Fagot-Bouquet, L., Audigier, R., Dhome, Y., & Lerasle, F. (2015). Online multi-person tracking based on global sparse collaborative representations. In  2015 IEEE International Conference on Image Processing (ICIP) (pp. 2414–2418). IEEE. from  doi.org/10.1109/ICIP.2015.7351235

✔MHTrevisited Vinet, L., & Zhedanov, A. (2015). Multiple Hypothesis Tracking Revisited Chanho,  22 (4), 625–638. from  doi.org/10.1088/1751-8113/44/8/085201  from  rehg.org/mht/

✔TMPORT Ristani, E., & Tomasi, C. (2015). Tracking multiple people online and in real time.  Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ,  9007 , 444–459. from  doi.org/10.1007/978-3-319-16814-2_29  from  vision.cs.duke.edu/DukeMTMC/

✔LDCT Solera, F. (2015). Learning to Divide and Conquer for Online Multi-Target Tracking.  2015 IEEE International Conference on Computer Vision (ICCV) , 4373–4381. from  github.com/francescosolera/LDCT  from  imagelab.ing.unimore.it/imagelab/researchActivity.asp?idActivity=09

✔headTracking Zhang, S., Wang, J., Wang, Z., Gong, Y., & Liu, Y. (2015). Multi-target tracking by learning local-to-global trajectory models.  Pattern Recognition ,  48 (2), 580–590. from  doi.org/10.1016/j.patcog.2014.08.013  from github.com/gengshan-y/headTracking

2014

✔CMOT Bae, S. H., & Yoon, K. J. (2014). Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning.  Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition , 1218–1225. from  doi.org/10.1109/CVPR.2014.159  from  cvl.gist.ac.kr/project/cmot.html

Tang, S., Andriluka, M., & Schiele, B. (2014). Detection and tracking of occluded people.  International Journal of Computer Vision ,  110 (1), 58–69. from  doi.org/10.1007/s11263-013-0664-6

✔H2T Wen, L., Li, W., Yan, J., Lei, Z., Yi, D., & Li, S. Z. (2014). Multiple target tracking based on undirected hierarchical relation hypergraph.  Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition , 1282–1289. from  doi.org/10.1109/CVPR.2014.167  from  cbsr.ia.ac.cn/users/lywen/

Yang, B., & Nevatia, R. (2014). Multi-target tracking by online learning a CRF model of appearance and motion patterns.  International Journal of Computer Vision ,  107 (2), 203–217. from  doi.org/10.1007/s11263-013-0666-4

✔CEM Chari, V., Lacoste-Julien, S., Laptev, I., & Sivic, J. (2014). On Pairwise Costs for Network Flow Multi-Object Tracking. Retrieved from  arxiv.org/abs/1408.3304  from  milanton.de/contracking/

✔OPCNF Chari, V., Lacoste-Julien, S., Laptev, I., & Sivic, J. (2014). Continuous Energy Minimization for Multi-Target Tracking, TPAMI 2014 from  milanton.de/files/pami2014/pami2014-anton.pdf  from  di.ens.fr/willow/research/flowtrack/

2013

Milan, A., Schindler, K., & Roth, S. (2013). Detection- and trajectory-level exclusion in multiple object tracking.  Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition , 3682–3689. from  doi.org/10.1109/CVPR.2013.472

Salvi, D., Waggoner, J., Temlyakov, A., & Wang, S. (2013). A graph-based algorithm for multi-target tracking with occlusion.  Proceedings of IEEE Workshop on Applications of Computer Vision , 489–496. from  doi.org/10.1109/WACV.2013.6475059

✔SMOT Dicle, C., Camps, O. I., & Sznaier, M. (2013). The way they move: Tracking multiple targets with similar appearance.  Proceedings of the IEEE International Conference on Computer Vision , 2304–2311. from  doi.org/10.1109/ICCV.2013.286 from bitbucket.org/cdicle/smot

2012

Yan, X., Wu, X., Kakadiaris, I. A., & Shah, S. K. (2012). To Track or To Detect ? An Ensemble Framework for Optimal Selection, 594–607.from  link.springer.com/conter/10.1007%2F978-3-642-33715-4_43

✔GMCP-Tracker Zamir, A. R., Dehghan, A., & Shah, M. (2012). GMCP-Tracker : Global Multi-object Tracking Using Generalized Minimum Clique Graphs, 343–356.from  crcv.ucf.edu/papers/eccv2012/GMCP-Tracker_ECCV12.pdf  from  crcv.ucf.edu/projects/GMCP-Tracker/

Hu, W., Li, X., Luo, W., Zhang, X., Maybank, S., & Zhang, Z. (2012). Single and multiple object tracking using log-euclidean riemannian subspace and block-division appearance model.  IEEE Transactions on Pattern Analysis and Machine Intelligence ,  34 (12), 2420–2440. from  doi.org/10.1109/TPAMI.2012.42

Yang, B., & Nevatia, R. (2012). Online learned discriminative part-based appearance models for multi-human tracking.  Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ,  7572 LNCS (PART 1), 484–498. from  doi.org/10.1007/978-3-642-33718-5_35

Shu, G., Dehghan, A., Oreifej, O., Hand, E., & Shah, M. (2012). Part-based multiple-person tracking with partial occlusion handling.  Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition , 1815–1821. from  doi.org/10.1109/CVPR.2012.6247879

✔OMPTTH Zhang, J., Lo Presti, L., & Sclaroff, S. (2012). Online multi-person tracking by tracker hierarchy.  Proceedings - 2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012 , 379–385. from  doi.org/10.1109/AVSS.2012.51  from  cs-people.bu.edu/jmzhang/tracker_hierarchy/Tracker_Hierarchy.htm

2011

Andriyenko, A., Roth, S., & Schindler, K. (2011). An analytical formulation of global occlusion reasoning for multi-target tracking.  Proceedings of the IEEE International Conference on Computer Vision , (November), 1839–1846. from  doi.org/10.1109/ICCVW.2011.6130472

Andriyenko, A., & Schindler, K. (2011). Multi-target tracking by continuous energy minimization. In  CVPR 2011 (pp. 1265–1272). IEEE. from  doi.org/10.1109/CVPR.2011.5995311

Pirsiavash, H., Ramanan, D., & Fowlkes, C. (2011). Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects.  Cvpr .from  people.csail.mit.edu/hpirsiav/papers/tracking_cvpr11.pdf

✔KSP Berclaz. (2011). Multiple Object Tracking using K-shortes Paths.  PAMI Preprint , 1–14. from  cvlab.epfl.ch/files/content/sites/cvlab2/files/publications/publications/2011/BerclazFTF11.pdf from cvlab.epfl.ch/software/ksp

2010

Mitzel, D., Horbert, E., Ess, A., & Leibe, B. (2010). Multi-person tracking with sparse detection and continuous segmentation.  Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ,  6311 LNCS (PART 1), 397–410. from  doi.org/10.1007/978-3-642-15549-9_29

MTDF Pedro F. Felzenszwalb, Ross B. Girshick, D. M. and D. R. (2010). Object detection with discriminatively trained part-based models. in TPAMI 2010.  doi.org/10.1109/MC.2014.42

2009

Hu, M., Ali, S., & Shah, M. (2009). Detecting global motion patterns in complex videos, 1–5. from  doi.org/10.1109/icpr.2008.4760950

Breitenstein, M. D., Reichlin, F., Leibe, B., Koller-Meier, E., & Van Gool, L. (2009). Robust tracking-by-detection using a detector confidence particle filter.  Proceedings of the IEEE International Conference on Computer Vision , (Iccv), 1515–1522. from  doi.org/10.1109/ICCV.2009.5459278

2008

M. IsardM. Isard, & J. M. (2008). B. A. B. M.-B. T. (application/pdf オブジェクト). R. from users.dickinson.edu/~jmac/publications/bramble.pdf ., & J. MacCormick. (2008). BraMBLe: A Bayesian Multiple-Blob Tracker (application/pdf オブジェクト). Retrieved from users.dickinson.edu/~jmac/publications/bramble.pdf

Zhang, L., Li, Y., & Nevatia, R. (2008). Global data association for multi-object tracking using network flows.  26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR . from doi.org/10.1109/CVPR.2008.4587584

还有一些对多目标跟踪的论文总结也很棒，推荐给大家。

http://bbs.cvmart.net/articles/265

github.com/huanglianghua/mot-papers/blob/master/README.md