多目标追踪资源列表(数据集、论文、代码和牛人主页等)

2019 年 2 月 7 日 极市平台
多目标追踪资源列表(数据集、论文、代码和牛人主页等)

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作者:SpyderXu
来源:https://github.com/ExtremeMart/multi-object-tracking-paper-list

原文:http://bbs.cvmart.net/articles/265


在MOT16, MOT17排行榜中可以找到一些新的作品,但是最近几个月似乎没有开源的工作,所以我没有更新源代码列表。MOT17基准提供了三种检测结果:DPM,更快的RCNN和SDP,如果你分析基于不同检测结果的结果,你会发现大多数跟踪器在更快的RCNN和SDP检测上有着相似的性能。跟踪器的良好排名主要依赖于DPM检测的良好性能,在采用更快的RCNN和SDP检测时,其性能与DeepSort相似。所以如果你想提高你的MOTA分数,可以关注DPM结果。


多目标跟踪是一项有着多年研究工作但还未真正成熟的计算机视觉任务,需要更多的开源和更多的标准评估指标。以下是我整理的多目标追踪资源列表,包含数据集、论文、代码和牛人主页等。

Datasets

PETS2009 : An old dataset.
http://www.cvg.reading.ac.uk/PETS2009/a.html


KITTI-Tracking : Multi-person or multi-car tracking dataset.
http://www.cvlibs.net/datasets/kitti/eval_tracking.php


MOT dataset :
A dataset for multi-person detection and tracking, mostly used.
https://motchallenge.net/


UA-DETRAC : A dataset for multi-car detection and tracking. 
http://detrac-db.rit.albany.edu/


AVSS2018 Challenge : AVSS2018 Challenge based on UA-DETRAC is opened!

https://iwt4s2018.wordpress.com/challenge/


DukeMTMC : A dataset for multi-camera multi-person tracking. 
http://vision.cs.duke.edu/DukeMTMC/


PoseTrack: A dataset for multi-person pose tracking. 
https://posetrack.net/


NVIDIA AI CITY Challenge: Challenges including "Traffic Flow Analysis", "Anomaly Detection" and "Multi-sensor Vehicle Detection and Reidentification", you may find some insteresting codes on their Github repos
链接:https://www.aicitychallenge.org/

GitHub:

https://github.com/NVIDIAAICITYCHALLENGE


Vis Drone: Tracking videos captured by drone-mounted cameras.
http://www.aiskyeye.com/views/index


JTA Dataset: A huge dataset for pedestrian pose estimation and tracking in urban scenarios created by exploiting the highly photorealistic video game Grand Theft Auto V developed by Rockstar North.

http://imagelab.ing.unimore.it/imagelab/page.asp?IdPage=25


Baidu Trajectory Interesting dataset for trajectory prediction for Autonomous drive, wait to be opened.
http://apolloscape.auto/trajectory.html


Path Track A new dataset with many scenes.
http://people.ee.ethz.ch/~daid/pathtrack/


Overview

P Emami,PM Pardalos,L Elefteriadou,S Ranka "Machine Learning Methods for Solving Assignment Problems in Multi-Target Tracking" 

[paper]:https://arxiv.org/pdf/1802.06897.pdf

Wenhan Luo, Junliang Xing, Anton Milan, Xiaoqin Zhang, Wei Liu, Xiaowei Zhao and Tae-Kyun Kim, "Multiple Object Tracking: A Literature Review" 

[paper]:

http://pdfs.semanticscholar.org/3dff/acda086689c1bcb01a8dad4557a4e92b8205.pdf


Evaluation Metric

CLEAR MOT : Bernardin, K. & Stiefelhagen, R. "Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metric"

 [paper]:

https://cvhci.anthropomatik.kit.edu/images/stories/msmmi/papers/eurasip2008.pdf


IDF1 : Ristani, E., Solera, F., Zou, R., Cucchiara, R. & Tomasi, C. "Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking" 

[paper]:

https://users.cs.duke.edu/~ristani/bmtt2016/ristani2016MTMC.pdf

Evaluation Code: https://github.com/cheind/py-motmetrics

Researcher

Anton Milan 

[webpage and his source code]:http://www.milanton.de/

Laura Leal-Taixé

 [webpage and her source code]:https://lealtaixe.github.io/publications/

Dynamic Vision and Learning Group 

[webpage and their source code]:https://dvl.in.tum.de/research/mot/

Longyin Wen

 [webpage and his source code]:http://www.cbsr.ia.ac.cn/users/lywen/

UCF

 [webpage]:http://crcv.ucf.edu/projects/tracking

Some source codes in above webpage are not listed in below Open Source.Such as:

"segTrack"

"Exploiting Hierarchical Dense Structures on Hypergraphs for Multi-Object Tracking"

"Learning an image-based motion context for multiple people tracking"

Open Source

Batch

NT: Longyin Wen, Dawei Du, Shengkun Li, Xiao Bian, Siwei Lyu Learning Non-Uniform Hypergraph for Multi-Object Tracking, In AAAI 2019 Waited!

[paper]:

http://www.cs.albany.edu/~lsw/papers/aaai19a.pdf

[code] :

https://github.com/longyin880815


headTracking: Shun Zhang, Jinjun Wang, Zelun Wang, Yihong Gong,Yuehu Liu: "Multi-Target Tracking by Learning Local-to-Global Trajectory Models" in PR 2015 seems like a repo.
[paper] :

https://www.researchgate.net/publication/265295656

[code] :

https://github.com/gengshan-y/headTracking


IOU : E. Bochinski, V. Eiselein, T. Sikora. "High-Speed Tracking-by-Detection Without Using Image Information" In International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017, 2017. 
 [paper]:

http://elvera.nue.tu-berlin.de/files/1517Bochinski2017.pdf

 [code]:

https://github.com/bochinski/iou-tracker/


NMGC-MOT:Andrii Maksai, Xinchao Wang, Franc¸ois Fleuret, and Pascal Fua "Non-Markovian Globally Consistent Multi-Object Tracking
" In ICCV 2017
[paper]:

http://openaccess.thecvf.com/content_ICCV_2017/papers/Maksai_Non-Markovian_Globally_Consistent_ICCV_2017_paper.pdf

[code] :

https://github.com/maksay/ptrack_cpp

D2T :Christoph Feichtenhofer, Axel Pinz, Andrew Zisserman, "Detect to Track and Track to Detect" In ICCV 2017
[paper]:

http://openaccess.thecvf.com/content_ICCV_2017/papers/Feichtenhofer_Detect_to_Track_ICCV_2017_paper.pdf

[code] :

https://github.com/feichtenhofer/Detect-Track


H2T : Longyin Wen, Wenbo Li, Junjie Yan, Zhen Lei, Dong Yi, Stan Z. Li. "Multiple Target Tracking Based on Undirected Hierarchical Relation Hypergraph," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
[paper]:

http://www.cbsr.ia.ac.cn/users/lywen/papers/CVPR2014_HyperGraphMultiTargetsTracker.pdf

[code] :

http://www.cbsr.ia.ac.cn/users/lywen/

LDCT : F. Solera, S. Calderara, R. Cucchiara "Learning to Divide and Conquer for Online Multi-Target Tracking"  In Proceedings of International Converence on Computer Vision (ICCV), Santiago Cile, Dec 12-18, 2015
[paper] :

http://ieeexplore.ieee.org/document/7410854/

[code page 1] :

https://github.com/francescosolera/LDCT

[code page 2]:

http://imagelab.ing.unimore.it/imagelab/researchActivity.asp?idActivity=09


CEM : Anton Milan, Stefan Roth, Konrad Schindler "Continuous Energy Minimization for Multi-Target Tracking" in pami 2014

[paper] :

http://www.milanton.de/files/pami2014/pami2014-anton.pdf

[code] :

http://www.milanton.de/contracking/


OPCNF : Chari, Visesh and Lacoste-Julien, Simon and Laptev, Ivan and Sivic, Josef "On Pairwise Costs for Network Flow Multi-Object Tracking" In CVPR 2015
[paper] :

https://arxiv.org/abs/1408.3304

[code] :

http://www.di.ens.fr/willow/research/flowtrack/


KSP : J. Berclaz, F. Fleuret, E. Türetken and P. Fua "Multiple Object Tracking using K-Shortest Paths Optimization" IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011.
[paper] :

https://cvlab.epfl.ch/files/content/sites/cvlab2/files/publications/publications/2011/BerclazFTF11.pdf

[code] :

https://cvlab.epfl.ch/software/ksp


GMCP : Amir Roshan Zamir, Afshin Dehghan, and Mubarak Shah "GMCP-Tracker: Global Multi-object Tracking Using Generalized Minimum Clique Graphs" European Conference on Computer Vision (ECCV), 2012.
[paper] :

http://crcv.ucf.edu/papers/eccv2012/GMCP-Tracker_ECCV12.pdf

[code] :

http://crcv.ucf.edu/projects/GMCP-Tracker/


Online

LSST :Weitao Feng, Zhihao Hu, Wei Wu, Junjie Yan, Wanli Ouyang "Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification" SOA tracker on MOT ranking list. No code now.
[paper] :

https://arxiv.org/abs/1901.06129


MOTBeyondPixels :Sarthak Sharma, Junaid Ahmed Ansari, J. Krishna Murthy, and K. Madhava Krishna Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking In ICRA 2018 

[paper]:

https://arxiv.org/abs/1802.09298

[code]:

https://github.com/JunaidCS032/MOTBeyondPixels

SST :Sun. S., Akhtar, N., Song, H., Mian A., & Shah M. (2018). Deep Affinity Network for Multiple Object Tracking: Interesting work and expect the author to update their DPM tracking results on MOT17 benchmark.
[paper]:

https://arxiv.org/abs/1810.11780

[code]:

https://github.com/shijieS/SST


MOTDT :Long Chen, Haizhou Ai "Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-identification" in ICME 2018!

[code]:

https://github.com/longcw/MOTDT

[paper]:

https://www.researchgate.net/publication/326224594_Real-time_Multiple_People_Tracking_with_Deeply_Learned_Candidate_Selection_and_Person_Re-identification

TMPORT : E. Ristani and C. Tomasi. Tracking Multiple People Online and in Real Time. in ACCV 2014 

[paper] :

https://users.cs.duke.edu/~tomasi/papers/ristani/ristaniAccv14.pdf

[code]:

http://vision.cs.duke.edu/DukeMTMC/

MOT-RNN : Anton Milan, Seyed Hamid Rezatofighi, Anthony Dick, Konrad Schindler, Ian Reid "Online Multi-target Tracking using Recurrent Neural Networks" In AAAI 2017.
[paper] :

http://www.milanton.de/files/aaai2017/aaai2017-anton-rnntracking.pdf

[code]:

https://bitbucket.org/amilan/rnntracking


DeepSort : Wojke, Nicolai and Bewley, Alex and Paulus, Dietrich "Simple Online and Realtime Tracking with a Deep Association Metric"  In ICIP 2017
[paper] :

https://arxiv.org/abs/1703.07402

[code]:

https://github.com/nwojke/deep_sort


Sort : Bewley, Alex and Ge, Zongyuan and Ott, Lionel and Ramos, Fabio and Upcroft, Ben "Simple Online and Realtime Tracking" In ICIP 2016.
[paper] :

https://arxiv.org/abs/1602.00763

[code]:

https://github.com/abewley/sort


MDP : Yu Xiang, Alexandre Alahi, and Silvio Savarese "Learning to Track: Online Multi-Object Tracking by Decision Making
"  In International Conference on Computer Vision (ICCV), 2015 

[paper] :

http://openaccess.thecvf.com/content_iccv_2015/papers/Xiang_Learning_to_Track_ICCV_2015_paper.pdf

[code]:

http://cvgl.stanford.edu/projects/MDP_tracking/


CMOT : S. H. Bae and K. Yoon. "Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning"  In CVPR 2014
[paper] :

https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Bae_Robust_Online_Multi-Object_2014_CVPR_paper.pdf

[code]:

https://cvl.gist.ac.kr/project/cmot.html


RCMSS : Mohamed A. Naiel1, M. Omair Ahmad, M.N.S. Swamy, Jongwoo Lim, and Ming-Hsuan Yang "Online Multi-Object Tracking Via
Robust Collaborative Model and Sample Selection" Computer Vision and Image Understanding 2016 

[paper] :

https://users.encs.concordia.ca/~rcmss/include/Papers/CVIU2016.pdf

[code]:

https://users.encs.concordia.ca/~rcmss/


MHT-DAM : Chanho Kim, Fuxin Li, Arridhana Ciptadi, James M. Rehg "Multiple Hypothesis Tracking Revisited" In ICCV 2015

[paper] :

https://www.cc.gatech.edu/~ckim314/papers/MHTR_ICCV2015.pdf

[code]:

http://bbs.cvmart.net/articles/265/rehg.org/mht/


OMPTTH : Jianming Zhang, Liliana Lo Presti and Stan Sclaroff, "Online Multi-Person Tracking by Tracker Hierarchy," [[paper]]() Proc. Int. Conf. on Advanced Video and Signal Based Surveillance (AVSS), 2012.

[code]:http://cs-people.bu.edu/jmzhang/tracker_hierarchy/Tracker_Hierarchy.htm


SMOT : C. Dicle, O. Camps, M. Sznaier. "The Way They Move: Tracking Targets with Similar Appearance"  In ICCV, 2013.
[paper] :

https://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Dicle_The_Way_They_2013_ICCV_paper.pdf

[code]:

https://bitbucket.org/cdicle/smot


Private Detection

POI : F. Yu, W. Li, Q. Li, Y. Liu, X. Shi, J. Yan. "POI: Multiple Object Tracking with High Performance Detection and Appearance Feature" In BMTT, SenseTime Group Limited, 2016
[paper] :

https://arxiv.org/pdf/1610.06136.pdf

[detection]:

https://drive.google.com/open?id=0B5ACiy41McAHMjczS2p0dFg3emM


New papers

CVPR2017

Eldar Insafutdinov, Mykhaylo Andriluka, Leonid Pishchulin, Siyu Tang, Evgeny Levinkov, Bjoern Andres, Bernt Schiele "Art Track: Articulated Multi-Person Tracking in the Wild" 

[paper]:https://arxiv.org/abs/1612.01465

Manmohan Chandraker, Paul Vernaza, Wongun Choi, Samuel Schulter "Deep Network Flow for Multi-Object Tracking" 

[paper]:

http://openaccess.thecvf.com/content_cvpr_2017/papers/Schulter_Deep_Network_Flow_CVPR_2017_paper.pdf

Jeany Son, Mooyeol Baek, Minsu Cho, and Bohyung Han, "Multi-Object Tracking with Quadruplet Convolutional Neural Networks" 

[paper]:

http://openaccess.thecvf.com/content_cvpr_2017/papers/Son_Multi-Object_Tracking_With_CVPR_2017_paper.pdf

ICCV2017

A. Sadeghian, A. Alahi, S. Savarese, Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies 

[paper] :

https://arxiv.org/abs/1701.01909

Andrii Maksai, Xinchao Wang, Franc¸ois Fleuret, and Pascal Fua "Non-Markovian Globally Consistent Multi-Object Tracking"

[paper]:

http://openaccess.thecvf.com/content_ICCV_2017/papers/Maksai_Non-Markovian_Globally_Consistent_ICCV_2017_paper.pdf

[code]:

https://github.com/maksay/ptrack_cpp

Christoph Feichtenhofer, Axel Pinz, Andrew Zisserman, "Detect to Track and Track to Detect" 

[paper]:

http://openaccess.thecvf.com/content_ICCV_2017/papers/Feichtenhofer_Detect_to_Track_ICCV_2017_paper.pdf

[code]:

https://github.com/feichtenhofer/Detect-Track

Qi Chu, Wanli Ouyang, Xiaogang Wang, Bin Liu, Nenghai Yu "Online Multi-Object Tracking Using CNN-Based Single Object Tracker With Spatial-Temporal Attention Mechanism" 

[paper]:

http://openaccess.thecvf.com/content_ICCV_2017/papers/Chu_Online_Multi-Object_Tracking_ICCV_2017_paper.pdf


CVPR2018

Ristani and C. Tomasi "Features for Multi-Target Multi-Camera Tracking and Re-Identification" 

[paper]:

https://arxiv.org/pdf/1803.10859.pdf

[code]:

https://github.com/ergysr/DeepCC

ECCV2018

Zhu, Ji and Yang, Hua and Liu, Nian and Kim, Minyoung and Zhang, Wenjun and Yang, Ming-Hsuan "Online Multi-Object Tracking with Dual Matching Attention Networks" The source code will be made available to the public!
 [paper]:

http://openaccess.thecvf.com/content_ECCV_2018/papers/Ji_Zhu_Online_Multi-Object_Tracking_ECCV_2018_paper.pdf


Ren, Liangliang and Lu, Jiwen and Wang, Zifeng and Tian, Qi and Zhou, Jie "Collaborative Deep Reinforcement Learning for Multi-Object Tracking" 

[paper]:

http://openaccess.thecvf.com/content_ECCV_2018/papers/Liangliang_Ren_Collaborative_Deep_Reinforcement_ECCV_2018_paper.pdf

Kim, Chanho and Li, Fuxin and Rehg, James M "Multi-object Tracking with Neural Gating Using Bilinear LSTM" 

[paper]:

http://openaccess.thecvf.com/content_ECCV_2018/papers/Chanho_Kim_Multi-object_Tracking_with_ECCV_2018_paper.pdf

New paper

M Fabbri, F Lanzi, S Calderara, A Palazzi "Learning to Detect and Track Visible and Occluded Body Joints in a Virtual World" Waited!
[paper] :

https://www.researchgate.net/publication/323957071_Learning_to_Detect_and_Track_Visible_and_Occluded_Body_Joints_in_a_Virtual_World


Cong Ma, Changshui Yang, Fan Yang, Yueqing Zhuang, Ziwei Zhang, Huizhu Jia, Xiaodong Xie "Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking" In ICME 2018 

[paper]:

https://arxiv.org/abs/1804.04555

Kuan Fang, Yu Xiang, Xiaocheng Li and Silvio Savarese "Recurrent Autoregressive Networks for Online Multi-Object Tracking" In IEEE Winter Conference on Applications of Computer Vision (WACV), 2018. 

[webpage]:

http://yuxng.github.io/

Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes "Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking" In WACV 2018 

[paper]:

https://arxiv.org/pdf/1803.03347.pdf

Multi-person Face Tracking

Shun Zhang, Yihong Gong, Jia-Bin Huang, Jongwoo Lim, Jinjun Wang, Narendra Ahuja and Ming-Hsuan Yang "Tracking Persons-of-Interest via Adaptive Discriminative Features" In ECCV 2016 

[paper]:

https://link.springer.com/content/pdf/10.1007%2F978-3-319-46454-1_26.pdf

[code]:

https://github.com/shunzhang876/AdaptiveFeatureLearning

Chung-Ching Lin, Ying Hung"A Prior-Less Method for Multi-Face Tracking in Unconstrained Videos" In CVPR 2018 

[paper]:

http://openaccess.thecvf.com/content_cvpr_2018/papers/Lin_A_Prior-Less_Method_CVPR_2018_paper.pdf


Multi-person Pose Tracking

Yuliang Xiu, Jiefeng Li, Haoyu Wang, Yinghong Fang, Cewu Lu "Pose Flow: Efficient Online Pose Tracking"  Idea is interesting but the true source code is not opened.
[paper]:

https://arxiv.org/abs/1802.00977


Bin Xiao, Haiping Wu, and Yichen Wei "Simple Baselines for Human Pose Estimation and Tracking" 

[paper]:

https://arxiv.org/pdf/1804.06208.pdf

[code]:

https://github.com/Microsoft/human-pose-estimation.pytorch





*延伸阅读

多目标追踪器:用 OpenCV 实现多目标追踪(C++/Python)

一种轻量级在线多目标车辆跟踪方法


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觉得有用麻烦给个好看啦~  

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相关内容

多目标跟踪,即Multiple Object Tracking(MOT),主要任务中是给定一个图像序列,找到图像序列中运动的物体,并将不同帧的运动物体进行识别,也就是给定一个确定准确的id,当然这些物体可以是任意的,如行人、车辆、各种动物等等,而最多的研究是行人跟踪,由于人是一个非刚体的目标,且实际应用中行人检测跟踪更具有商业价值。
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