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/1802.0689

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

?DeepMOT Xu, Y., Ban, Y., Alameda-Pineda, X., & Horaud, R. (2019). DeepMOT: A Differentiable Framework for Training Multiple Object Trackers, (i). Retrieved from DeepMOT: A Differentiable Framework for Training Multiple Object Tracker XU Yihong

?FANTrack Baser, E., Balasubramanian, V., Bhattacharyya, P., & Czarnecki, K. (2019). FANTrack: 3D Multi-Object Tracking with Feature Association Network. Retrieved from FANTrack: 3D Multi-Object Tracking with Feature Association Network 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/1905.0229

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/1904.0498

STRN Xu, J., Cao, Y., Zhang, Z., & Hu, H. (2019). Spatial-Temporal Relation Networks for Multi-Object Tracking. Retrieved from arxiv.org/abs/1904.1148

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/1902.0823

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/1901.0612

?NT Longyin Wen, Dawei Du, Shengkun Li, Xiao Bian, Siwei Lyu Learning Non-Uniform Hypergraph for Multi-Object Tracking, In AAAI 2019 from cs.albany.edu/~lsw/pape from github.com/longyin88081

?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/1902.0360 VisualComputingInstitute/TrackR-CNN

GM-PHD-N1F/T Baisa, N. L., & Wallace, A. (2019). Development of a N-type GM-PHD filter for multiple target, multiple type visual tracking. Journal of Visual Communication and Image Representation, 59, 257–271. Redirecting

MTDF Fu, Z., Angelini, F., Chambers, J., & Naqvi, S. M. (2019). Multi-Level Cooperative Fusion of GM-PHD Filters for Online Multiple Human Tracking. IEEE Transactions on Multimedia, (Dcm), 1–1. Multi-Level Cooperative Fusion of GM-PHD Filters for Online Multiple Human Tracking

FPSN Lee, S., & Kim, E. (2019). Multiple object tracking via feature pyramid siamese networks. IEEE Access, 7, 8181–8194. Multiple Object Tracking via Feature Pyramid Siamese Networks

2018

DeepCC Ristani, E., & Tomasi, C. (2018). Features for Multi-Target Multi-Camera Tracking and Re-Identification. from doi.org/10.1109/CVPR.20

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 doi.org/10.1109/AVSS.20

?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/1810.1178 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/10.1007/978-3-0 jizhu1023/DMAN_MOT

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/1811.0725

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.2

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.2

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/1808.0156

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.2

?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/1802.0929 from github.com/JunaidCS032/

?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/1809.0442 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/1804.1013 from github.com/sbreuers/det

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/c

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

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/public

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

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/1804.0455

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/1803.0334

Maksai, A., & Fua, P. (2018). Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking. Retrieved from Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking

Wan, X., Wang, J., & Zhou, S. (2018). An online and flexible multi-object tracking framework using long short-term memory. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2018–June, 1311–1319. doi.org/10.1109/CVPRW.2

?V-IOU Bochinski, E., Senst, T., & Sikora, T. (2018). Extending IOU Based Multi-Object Tracking by Visual Information. In 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (pp. 1–6). IEEE. Extending IOU Based Multi-Object Tracking by Visual Information github.com/bochinski/io

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.20

?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.20 from github.com/nwojke/deep_

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.20

SOTforMOT He, Q., Wu, J., Yu, G., & Zhang, C. (2017). SOT for MOT. Retrieved from arxiv.org/abs/1712.0105

?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/c from github.com/maksay/ptrac

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.20

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.20

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.20

?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.20 from github.com/bochinski/io

?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/1604.0363 from bitbucket.org/amilan/rn

?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.20 from github.com/feichtenhofe

?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. from users.encs.concordia.ca

?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/1705.0460 from github.com/VisualComput

?CIWT Aljoˇsa Oˇsep, Alexander Hermans Combined Image and World-Space Tracking in Traffic Scenes In ICRA 2017 from vision.rwth-aachen.de/m from github.com/aljosaosep/c

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/10.1007/978-3-3

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/10.1007/978-3-3

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/10.1007/978-3-3

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.20

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/1603.0083

?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.20 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/1612.0146

SiameseCNN Leal-Taixe, L., Canton-Ferrer, C., & Schindler, K. (2016). Learning by Tracking: Siamese CNN for Robust Target Association. In 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (pp. 418–425). IEEE. Learning by Tracking: Siamese CNN for Robust Target Association

2015

Fagot-bouquet, L., Audigier, R., Dhome, Y., & Multi-person, F. L. O. (2018). Online Multi-person Tracking Based on Global Sparse Collaborative Representations, 2015 IEEE International Conference on Image Processing (ICIP) from ieeexplore.ieee.org/doc

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/10.1007/978-3-3

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/1504.0194

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.20

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.20

?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.20 from cvgl.stanford.edu/proje

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.20

?MHTrevisited Vinet, L., & Zhedanov, A. (2015). Multiple Hypothesis Tracking Revisited Chanho, 22(4), 625–638. from doi.org/10.1088/1751-81 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/10.1007/978-3-3 from vision.cs.duke.edu/Duke

?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 https://doi.org/10.1109/ICCV.2015.497 from <https://github.com/francescosolera/LDCT from imagelab.ing.unimore.it

?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.patco from github.com/gengshan-y/h

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.20 from cvl.gist.ac.kr/project/

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-

?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.20 from cbsr.ia.ac.cn/users/lyw

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-

?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/pami2 from di.ens.fr/willow/resear

?Occlusion GeodesicsPossegger, H., Mauthner, T., Roth, P. M., & Bischof, H. (2014). Occlusion geodesics for online multi-object tracking. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1306–1313. Occlusion Geodesics for Online Multi-object Tracking lrs.icg.tugraz.at/downl

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.20

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.20

?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.20 from bitbucket.org/cdicle/sm

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/conte

?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/ecc from crcv.ucf.edu/projects/G

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.2

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/10.1007/978-3-6

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.20

?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.20 from cs-people.bu.edu/jmzhan

GM-PHD Eiselein, V., Arp, D., P?tzold, M., & Sikora, T. (2012). Real-time multi-human tracking using a probability hypothesis density filter and multiple detectors. Proceedings - 2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012, (3), 325–330. Real-Time Multi-human Tracking Using a Probability Hypothesis Density Filter and Multiple Detectors

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.2

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.20

Pirsiavash, H., Ramanan, D., & Fowlkes, C. (2011). Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. Cvpr.from baidu.com/link?

?KSP Berclaz. (2011). Multiple Object Tracking using K-shortes Paths. PAMI Preprint, 1–14. from cvlab.epfl.ch/files/con from cvlab.epfl.ch/software/

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/10.1007/978-3-6

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

2009

Hu, M., Ali, S., & Shah, M. (2009). Detecting global motion patterns in complex videos, 1–5. from doi.org/10.1109/icpr.20

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.20

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/~jm ., & J. MacCormick. (2008). BraMBLe: A Bayesian Multiple-Blob Tracker (application/pdf オブジェクト). Retrieved from users.dickinson.edu/~jm

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.20

还有一些对多目标跟踪的论文总结也很棒，推荐给大家。

github.com/SpyderXu/mul

github.com/huanglianghu

转载请联系作者并注明出处，侵权必究。


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