多目标跟踪近年论文及开源代码汇总

2019 年 5 月 12 日 极市平台

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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

*延伸阅读

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