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awesome-multiple-object-tracking

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Resources for Multiple Object Tracking (MOT)

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# Awesome Multiple object Tracking: [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

> A curated list of multi-object-tracking and related area resources. It only contains online methods. 中文版更为详细，具体查看仓库根目录下的[README-zh.md](./README-zh.md)文件。

<a id="markdown-contents" name="contents"></a>
## Contents

- [Awesome Multiple object Tracking: ](#awesome-multiple-object-tracking-)
 - [Contents](#contents)
 - [Review papers](#review-papers)
 - [Algorithm papers](#algorithm-papers)
 - [**2024**](#2024)
 - [**2023**](#2023)
 - [**2022**](#2022)
 - [**2021**](#2021)
 - [**2020**](#2020)
 - [**2019**](#2019)
 - [**2018**](#2018)
 - [**2017**](#2017)
 - [**2016**](#2016)
 - [Datasets](#datasets)
 - [Surveillance Scenarios](#surveillance-scenarios)
 - [Driving Scenarios](#driving-scenarios)
 - [Sport Scenarios](#sport-scenarios)
 - [Metrics](#metrics)
 - [Benchmark Results](#benchmark-results)
 - [MOT16](#mot16)
 - [MOT17](#mot17)
 - [MOT20](#mot20)
 - [Toolbox](#toolbox)
 - [Course](#course)

<a id="markdown-review-papers" name="review-papers"></a>
## Review papers

Multiple Object Tracking: A Literature Review [[paper](https://arxiv.org/pdf/1409.7618.pdf)]

Deep Learning in Video Multi-Object Tracking: A Survey [[paper](https://arxiv.org/pdf/1907.12740.pdf)]

Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking [[paper](https://arxiv.org/pdf/1704.02781.pdf)]

Machine Learning Methods for Data Association in Multi-Object Tracking [[paper](https://arxiv.org/pdf/1802.06897v2)]

MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking [[paper](https://arxiv.org/pdf/2010.07548.pdf)]  **new paper for new MOT researcher**

Multiple Object Tracking in Deep Learning Approaches:A Survey [[paper](https://www.mdpi.com/2079-9292/10/19/2406/htm)]

Object Tracking: A Comprehensive Survey From Classical Approaches to Large Vision-Language and Foundation Models [[paper](https://www.preprints.org/manuscript/202509.2051/v1)]

<a id="markdown-algorithm-papers" name="algorithm-papers"></a>
## Algorithm papers

<a id="markdown-2025" name="2025"></a>
### **2025**

**MOTIP**: Multiple Object Tracking as ID Prediction [[paper](https://arxiv.org/abs/2403.16848)] [[code](https://github.com/MCG-NJU/MOTIP)] **CVPR 2025**

**SambaMOTR**: Samba: Synchronized Set-of-Sequences Modeling for Multiple Object Tracking [[paper](https://arxiv.org/abs/2410.01806)] [[code](https://github.com/mattiasegu/sambamotr)] **ICLR 2025**

**CO-MOT**: CO-MOT: Boosting End-to-end Transformer-based Multi-Object Tracking via Coopetition Label Assignment and Shadow Sets [[paper](https://iclr.cc/virtual/2025/poster/31235)] [[code](https://github.com/BingfengYan/CO-MOT)] **ICLR 2025**

<a id="markdown-2024" name="2024"></a>
### **2024**

**MASA**: Matching Anything by Segmenting Anything [[paper](https://arxiv.org/abs/2406.04221)] [[code](https://github.com/siyuanliii/masa)] **CVPR 2024**

**GeneralTrack**: Towards Generalizable Multi-Object Tracking [[paper](https://arxiv.org/abs/2406.00429)] [[code](https://github.com/qinzheng2000/GeneralTrack)] **CVPR 2024**

**PASTA**: Is Multiple Object Tracking a Matter of Specialization? [[paper](https://arxiv.org/pdf/2411.00553)] [[code](https://aimagelab.github.io/PASTA/)] **NeurIPS 2024**

**Hybrid-SORT**: Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking [[paper](https://browse.arxiv.org/abs/2308.00783)] [[code](https://github.com/ymzis69/HybridSORT)] **AAAI 2024**

**UCMCTrack**: UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation [[paper](https://browse.arxiv.org/abs/2312.08952)] [[code](https://github.com/corfyi/UCMCTrack)] **AAAI 2024**

### **2023**

**ColTrack**: Collaborative Tracking Learning for Frame-Rate-Insensitive Multi-Object Tracking [[paper](https://arxiv.org/abs/2308.05911)] **ICCV2023**

**MeMOTR**: MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking [[code](https://github.com/MCG-NJU/MeMOTR)] [[paper](https://arxiv.org/abs/2307.15700)] **ICCV2023**

**TrackFlow**: TrackFlow: Multi-Object Tracking with Normalizing Flows [[paper](https://arxiv.org/abs/2308.11513)] **ICCV2023**

**MotionTrack**: Learning Robust Short-term and Long-term Motions for Multi-Object Tracking [[paper](https://arxiv.org/pdf/2303.10404.pdf)] **CVPR2023**

**C-BIoU**: Hard to Track Objects with Irregular Motions and Similar Appearances? Make It Easier by Buffering the Matching Space [[paper](https://arxiv.org/pdf/2211.14317.pdf)] **WACV 2023**

**GHOST**: Simple Cues Lead to a Strong Multi-Object Tracker [[code](https://github.com/dvl-tum/GHOST)] [[paper](https://arxiv.org/pdf/2206.04656.pdf)] **CVPR2023**

**Deep OC-SORT**: Multi-Pedestrian Tracking by Adaptive Re-Identification [[code](https://github.com/GerardMaggiolino/Deep-OC-SORT/)] [[paper](https://arxiv.org/pdf/2302.11813)] **ICIP2023**

### **2022**

**MTracker**: Robust Multi-Object Tracking by Marginal Inference [[code]()] [[paper](https://arxiv.org/pdf/2208.03727.pdf)] **ECCV 2022**

**Unicorn**: Towards Grand Unification of Object Tracking [[code](https://github.com/MasterBin-IIAU/Unicorn)] [[paper](https://arxiv.org/pdf/2207.07078.pdf)] **ECCV2022 Oral**

**P3AFormer**: Tracking Objects as Pixel-wise Distributions [[code](https://sjtuytc.github.io/zelin_pages/p3aformer.html)] [[paper](https://arxiv.org/pdf/2207.05518.pdf)] **ECCV2022 oral**

**BoT-SORT**: BoT-SORT: Robust Associations Multi-Pedestrian Tracking [[code](https://github.com/NirAharon/BOT-SORT)] [[paper](https://arxiv.org/pdf/2206.14651.pdf)]

**SGT**: Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker [[code](https://github.com/HYUNJS/SGT)] [[paper](https://arxiv.org/pdf/2205.00968.pdf)]

**LPT**: Learning of Global Objective for Network Flow in Multi-Object Tracking [[code](https://github.com/derkbreeze/LPT)] [[paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Learning_of_Global_Objective_for_Network_Flow_in_Multi-Object_Tracking_CVPR_2022_paper.pdf)] 	**CVPR 2022**

**MeMOT**: MeMOT: Multi-Object Tracking with Memory [[paper](https://arxiv.org/pdf/2203.16761.pdf)] **CVPR2022 oral**

**UTT**: Unified Transformer Tracker for Object Tracking [[code](https://github.com/Flowerfan/Trackron)] [[paper](https://arxiv.org/pdf/2203.15175.pdf)] **CVPR2022**

**OC-SORT**: Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking [[code](https://github.com/noahcao/OC_SORT)] [[paper](https://arxiv.org/pdf/2203.14360.pdf)]

**GTR**: Global Tracking Transformers [[code](https://github.com/xingyizhou/GTR)] [[paper](https://arxiv.org/pdf/2203.13250.pdf)] 	**CVPR 2022**

**StrongSORT**: StrongSORT: Make DeepSORT Great Again [[code](https://github.com/dyhBUPT/StrongSORT)]  [[paper](https://arxiv.org/pdf/2202.13514.pdf)]

**MAA**: Modelling Ambiguous Assignments for Multi-Person Tracking in Crowds [[code]] [[paper](https://openaccess.thecvf.com/content/WACV2022W/HADCV/papers/Stadler_Modelling_Ambiguous_Assignments_for_Multi-Person_Tracking_in_Crowds_WACVW_2022_paper.pdf)]

**CrowdTrack**: On the Performance of Crowd-Specific Detectors in Multi-Pedestrian Tracking [[code]()] [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9663836)]

<a id="markdown-2021" name="2021"></a>
### **2021**

**ByteTrack:** ByteTrack: Multi-Object Tracking by Associating Every Detection Box [[code](https://github.com/ifzhang/ByteTrack)] [[paper](https://arxiv.org/pdf/2110.06864.pdf)]  **new SOTA**

**PCAN** Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation [[code](https://github.com/SysCV/pcan)] [[paper](https://papers.nips.cc/paper/2021/file/093f65e080a295f8076b1c5722a46aa2-Paper.pdf)] **NeurIPS2021**

**PermaTrack**: Learning to Track with Object Permanence [[code](https://github.com/TRI-ML/permatrack)] [[paper](https://arxiv.org/pdf/2103.14258.pdf)]  **ICCV2021**

**TMOH**: Improving Multiple Pedestrian Tracking by Track Management and Occlusion Handling [code] [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Stadler_Improving_Multiple_Pedestrian_Tracking_by_Track_Management_and_Occlusion_Handling_CVPR_2021_paper.pdf)]  **CVPR2021**

**SOTMOT**: Improving Multiple Object Tracking with Single Object Tracking [code] [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Improving_Multiple_Object_Tracking_With_Single_Object_Tracking_CVPR_2021_paper.pdf)] **CVPR2021**

**LPC_MOT**: Learning a Proposal Classifier for Multiple Object Tracking [[code](https://github.com/daip13/LPC_MOT)] [[paper](https://arxiv.org/pdf/2103.07889.pdf)] **CVPR2021**

**MTP**: Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking [[code](https://github.com/chkim403/blstm-mtp)] [[paper](https://arxiv.org/pdf/2101.12159.pdf)] **CVPR2021**

**TADAM**: Online Multiple Object Tracking with Cross-Task Synergy [[code](https://github.com/songguocode/TADAM)] [[paper](https://arxiv.org/pdf/2104.00380.pdf)] **CVPR2021**

**RelationTrack**: RelationTrack: Relation-aware Multiple Object Tracking with Decoupled Representation [[code]] [[paper](https://arxiv.org/pdf/2105.04322.pdf)]

**MOTR**: MOTR: End-to-End Multiple-Object Tracking with TRansformer [[code](https://github.com/megvii-model/MOTR)]  [[paper](https://arxiv.org/pdf/2105.03247.pdf)]

**OMC**: One More Check: Making "Fake Background" Be Tracked Again [[code](https://github.com/JudasDie/SOTS)] [[paper](https://arxiv.org/pdf/2104.09441.pdf)] **AAAI2022**

**QDTrack**: Quasi-Dense Similarity Learning for Multiple Object Tracking [[code](https://github.com/SysCV/qdtrack)] [[paper](https://arxiv.org/pdf/2006.06664.pdf)] **CVPR2021**

**SiamMOT**: SiamMOT: Siamese Multi-Object Tracking [[code](https://github.com/amazon-research/siam-mot)] [[paper](https://arxiv.org/pdf/2105.11595.pdf)] **CVPR2021**

**GMTracker**: Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking [[code](https://github.com/jiaweihe1996/GMTracker)] [[paper](https://arxiv.org/pdf/2103.16178.pdf)] **CVPR2021**

**ArTIST**: Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking [[paper](https://arxiv.org/pdf/2012.02337.pdf)] **CVPR2021**

**CorrTracker/TLR**: Multiple Object Tracking with Correlation Learning [code] [[paper](https://arxiv.org/pdf/2104.03541.pdf)] **CVPR2021**

**TransMOT**：Spatial-Temporal Graph Transformer for Multiple Object Tracking [code] [[paper](https://arxiv.org/pdf/2104.00194v2.pdf)]

**TransCenter**: TransCenter: Transformers with Dense Queries for Multiple-Object Tracking [[code](https://github.com/yihongXU/TransCenter)] [[paper](https://arxiv.org/pdf/2103.15145.pdf)]

**GCNet**: Global Correlation Network: End-to-End Joint Multi-Object Detection and Tracking [code] [[paper](https://arxiv.org/pdf/2103.12511.pdf)]

**TraDes**: Track to Detect and Segment: An Online Multi-Object Tracker [[code](https://github.com/JialianW/TraDeS)]  [[paper](https://arxiv.org/pdf/2103.08808.pdf)] **CVPR2021**

**DEFT**: DEFT: Detection Embeddings for Tracking [[code](https://github.com/MedChaabane/DEFT)] [[paper](https://arxiv.org/pdf/2102.02267.pdf)]

**TrackMPNN**: TrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Tracking [[code](https://github.com/arangesh/TrackMPNN)] [[paper](https://arxiv.org/pdf/2101.04206.pdf)]

**TrackFormer**: TrackFormer: Multi-Object Tracking with Transformers [[code](https://github.com/timmeinhardt/trackformer)] [[paper](https://arxiv.org/pdf/2101.02702.pdf)]

<a id="markdown-2020" name="2020"></a>
### **2020**

**ReMOTS**: ReMOTS: Self-Supervised Refining Multi-Object Tracking and Segmentation (1st-place solution for CVPR 2020 MOTS Challenge)[[paper](https://arxiv.org/pdf/2007.03200.pdf)]

**TransTrack**: TransTrack: Multiple-Object Tracking with Transformer [[code](https://github.com/PeizeSun/TransTrack)] [[paper](https://arxiv.org/pdf/2012.15460.pdf)]

**TPAGT**: Tracklets Predicting Based Adaptive Graph Tracking [[paper](https://arxiv.org/pdf/2010.09015v3.pdf)] **original FGAGT**

**MLT**: Multiplex Labeling Graph for Near-Online Tracking in Crowded Scenes [[paper](https://ieeexplore.ieee.org/document/9098857)]

**GSDT**: Joint Object Detection and Multi-Object Tracking with Graph Neural Networks [[code](https://github.com/yongxinw/GSDT)] [[paper](http://arxiv.org/pdf/2006.13164)]

**SMOT**: SMOT: Single-Shot Multi Object Tracking [[paper](http://arxiv.org/pdf/2010.16031)]

**CSTrack**: Rethinking the competition between detection and ReID in Multi-Object Tracking [[code](https://github.com/JudasDie/SOTS)][[paper](http://arxiv.org/pdf/2010.12138)]

**MAT**: MAT: Motion-Aware Multi-Object Tracking [[paper](https://arxiv.org/ftp/arxiv/papers/2009/2009.04794.pdf)]

**UnsupTrack**: Simple Unsupervised Multi-Object Tracking [[paper](https://arxiv.org/pdf/2006.02609.pdf)]

**FairMOT**: FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking [[code](https://github.com/ifzhang/FairMOT)][[paper](https://arxiv.org/pdf/2004.01888v5.pdf)] **a new version of FairMOT, compared with new method like CTracker**

**DMM-Net**: Simultaneous Detection and Tracking with Motion Modelling for Multiple Object Tracking [[code](https://github.com/shijieS/DMMN)][[paper](https://arxiv.org/abs/2008.08826)]

**SoDA**: SoDA: Multi-Object Tracking with Soft Data Association [[code]][[paper](https://arxiv.org/abs/2008.07725)]

**CTracker**: Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking [[code](https://github.com/pjl1995/CTracker)][[paper](http://arxiv.org/abs/2007.14557)]

**MPNTracker**: Learning a Neural Solver for Multiple Object Tracking [[code]](https://github.com/dvl-tum/mot_neural_solver)[[paper]](https://arxiv.org/pdf/1912.07515.pdf)

**UMA**: A Unified Object Motion and Affinity Model for Online Multi-Object Tracking [[code]](https://github.com/yinjunbo/UMA-MOT)[[paper]](https://arxiv.org/pdf/2003.11291.pdf)

**RetinaTrack**: Online Single Stage Joint Detection and Tracking [[code]][[paper]](https://arxiv.org/pdf/2003.13870.pdf)

**FairMOT**: A Simple Baseline for Multi-Object Tracking [[code]](https://github.com/ifzhang/FairMOT)[[paper]](https://arxiv.org/pdf/2004.01888.pdf)

**TubeTK**: TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model [[code](https://github.com/BoPang1996/TubeTK)][[paper](https://arxiv.org/pdf/2006.05683.pdf)]

**CenterTrack**: Tracking Objects as Points [[code]](https://github.com/xingyizhou/CenterTrack)[[paper]](https://arxiv.org/pdf/2004.01177.pdf)

**Lif_T**: Lifted Disjoint Paths with Application in Multiple Object Tracking [[code]](https://github.com/AndreaHor/LifT_Solver)[[paper]](https://arxiv.org/pdf/2006.14550.pdf)

**PointTrack**: Segment as points for efficient online multi-object tracking and segmentation [[code]](https://github.com/detectRecog/PointTrack)[[paper]](https://arxiv.org/pdf/2007.01550.pdf)

**PointTrack++**: PointTrack++ for Effective Online Multi-Object Tracking and Segmentation [[code]](https://github.com/detectRecog/PointTrack)[[paper]](https://arxiv.org/pdf/2007.01549.pdf)

**FFT**: Multiple Object Tracking by Flowing and Fusing [[paper]](https://link.zhihu.com/?target=https%3A//arxiv.org/pdf/2001.11180)

**MIFT**: Refinements in Motion and Appearance for Online Multi-Object Tracking [[code]](https://github.com/nightmaredimple/libmot)[[paper]](https://arxiv.org/pdf/2003.07177.pdf)

**EDA_GNN**: Graph Neural Based End-to-end Data Association Framework for Online Multiple-Object Tracking [[code]](https://github.com/peizhaoli05/EDA_GNN)[[paper]](https://arxiv.org/pdf/1907.05315.pdf)

**GNMOT**: Graph Networks for Multiple Object Tracking [[code]](https://github.com/yinizhizhu/GNMOT)[[paper]](https://openaccess.thecvf.com/content_WACV_2020/html/Li_Graph_Networks_for_Multiple_Object_Tracking_WACV_2020_paper.html)

<a id="markdown-2019" name="2019"></a>
### **2019**

**Tracktor/Tracktor++**: Tracking without bells and whistles [[code]](https://github.com/phil-bergmann/tracking_wo_bnw)[[paper]](https://arxiv.org/pdf/1903.05625.pdf)

**DeepMOT**: How To Train Your Deep Multi-Object Tracker [[code]](https://github.com/yihongXU/deepMOT)[[paper]](https://arxiv.org/pdf/1906.06618.pdf)

**JDE**: Towards Real-Time Multi-Object Tracking [[code]](https://github.com/Zhongdao/Towards-Realtime-MOT)[[paper]](https://arxiv.org/pdf/1909.12605v1.pdf)

**MOTS**: MOTS: Multi-Object Tracking and Segmentation[[paper]](https://arxiv.org/pdf/1902.03604.pdf)

**FANTrack**: FANTrack: 3D Multi-Object Tracking with Feature Association Network [[code]](https://git.uwaterloo.ca/wise-lab/fantrack)[[paper]](https://arxiv.org/pdf/1905.02843.pdf)

**FAMNet**: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking[[paper]](https://arxiv.org/pdf/1904.04989.pdf)

<a id="markdown-2018" name="2018"></a>
### **2018**

**DeepCC**: Features for Multi-Target Multi-Camera Tracking and Re-Identification [[paper]](https://openaccess.thecvf.com/content_cvpr_2018/papers/Ristani_Features_for_Multi-Target_CVPR_2018_paper.pdf)

**SADF**: Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering [[paper]](https://arxiv.org/pdf/1805.10916.pdf)

**DAN**: Deep Affinity Network for Multiple Object Tracking [[code]](https://link.zhihu.com/?target=https%3A//github.com/shijieS/SST.git)[[paper]](https://link.zhihu.com/?target=http%3A//arxiv.org/abs/1810.11780)

**DMAN**: Online Multi-Object Tracking with Dual Matching Attention Networks [[code]](https://github.com/jizhu1023/DMAN_MOT)[[paper]](https://openaccess.thecvf.com/content_ECCV_2018/papers/Ji_Zhu_Online_Multi-Object_Tracking_ECCV_2018_paper.pdf)

**BeyondPixels**: Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking [[code]](https://github.com/JunaidCS032/MOTBeyondPixels)[[paper]](http://arxiv.org/abs/1802.09298)

**MOTDT**: Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification [[code]](https://github.com/longcw/MOTDT)[[paper]](https://arxiv.org/abs/1809.04427)

**DetTA**: Detection-Tracking for Efficient Person Analysis: The DetTA Pipeline [[code]](https://github.com/sbreuers/detta)[[paper]](https://arxiv.org/abs/1804.10134)

**V-IOU**: Extending IOU Based Multi-Object Tracking by Visual Information [[code]](https://github.com/bochinski/iou-tracker/)[[paper]](http://elvera.nue.tu-berlin.de/files/1547Bochinski2018.pdf)

<a id="markdown-2017" name="2017"></a>
### **2017**

**DeepSORT**: Simple Online and Realtime Tracking with a Deep Association Metric [[code]](https://github.com/nwojke/deep_sort)[[paper]](https://arxiv.org/pdf/1703.07402.pdf)

**NMGC-MOT**: Non-Markovian Globally Consistent Multi-Object Tracking [[code]](https://github.com/maksay/ptrack_cpp)[[paper]](https://openaccess.thecvf.com/content_ICCV_2017/papers/Maksai_Non-Markovian_Globally_Consistent_ICCV_2017_paper.pdf)

**IOUTracker**: High-Speed tracking-by-detection without using image information [[code]](https://github.com/bochinski/iou-tracker/)[[paper]](http://elvera.nue.tu-berlin.de/typo3/files/1517Bochinski2017.pdf)

**RNN_LSTM**: Online Multi-Target Tracking Using Recurrent Neural Networks [[code]](https://bitbucket.org/amilan/rnntracking)[[paper]](https://arxiv.org/abs/1604.03635)

**D2T**: Detect to Track and Track to Detect [[code]](https://github.com/feichtenhofer/Detect-Track)[[paper]](https://openaccess.thecvf.com/content_ICCV_2017/papers/Feichtenhofer_Detect_to_Track_ICCV_2017_paper.pdf)

**RCMSS**: Online multi-object tracking via robust collaborative model and sample selection [[paper]](https://faculty.ucmerced.edu/mhyang/papers/cviu16_MOT.pdf)

**CIWT**: Combined image-and world-space tracking in traffic scenes [[code]](https://github.com/aljosaosep/ciwt)[[paper]](https://arxiv.org/pdf/1809.07357.pdf)

<a id="markdown-2016" name="2016"></a>
### **2016**

**SORT**: Simple online and realtime tracking [[code]](https://link.zhihu.com/?target=https%3A//github.com/abewley/sort)[[paper]](https://arxiv.org/pdf/1602.00763.pdf)

**POI**: POI: Multiple Object Tracking with High Performance Detection and Appearance Feature [[code](https://arxiv.org/pdf/1610.06136)]

<a id="markdown-datasets" name="datasets"></a>
## Datasets

<a id="markdown-surveillance-scenarios" name="surveillance-scenarios"></a>
### Surveillance Scenarios

PETS 2009 Benchmark Data [[url]](http://www.cvg.reading.ac.uk/PETS2009/a.html) 
MOT Challenge [[url]](https://motchallenge.net/) 
UA-DETRAC [[url]](http://detrac-db.rit.albany.edu/download) 
WILDTRACK [[url]](https://www.epfl.ch/labs/cvlab/data/data-wildtrack/) 
NVIDIA AI CITY Challenge [[url]](https://www.aicitychallenge.org/2020-data-and-evaluation/) 
VisDrone [[url]](https://github.com/VisDrone) 
JTA Dataset [[url]](https://github.com/fabbrimatteo/JTA-Dataset) 
Path Track [[url]](https://www.trace.ethz.ch/publications/2017/pathtrack/index.html) 
TAO [[url]](https://github.com/TAO-Dataset/tao) 
GMOT40 [[url]](https://arxiv.org/abs/2011.11858) 
TAO-OW [[url](https://openworldtracking.github.io/)]

<a id="markdown-driving-scenarios" name="driving-scenarios"></a>
### Driving Scenarios

KITTI-Tracking [[url]](http://www.cvlibs.net/datasets/kitti/eval_tracking.php) 
APOLLOSCAPE [[url]](http://apolloscape.auto/tracking.html) 
APOLLO MOTS [[url]](https://github.com/detectRecog/PointTrack) 
Omni-MOT [[url]](https://pan.baidu.com/s/1ma0rZIW6vfXeq5tdEk6K2w) 
BDD100K [[url]](http://bdd-data.berkeley.edu/) 
Waymo [[url]](https://waymo.com/open/download/#)

<a id="markdown-sport-scenarios" name="sport-scenarios"></a>
### Sport Scenarios

SoccerNet Minimap Tracking [[url]](https://github.com/SoccerNet/sn-gamestate) 
SoccerNet Tracking [[url]](https://github.com/SoccerNet/sn-tracking)

<a id="markdown-metrics" name="metrics"></a>
## Metrics

| metric|formula|
| :---:|:---:|
| accuracy| $ Accuracy = {{TP + TN} \over {TP + TN + FP + FN}} $|
| recall | $ Recall = {TP \over {TP + FN}} = TPR$|
|precision|$ Precision = {TP \over {TP + FP}} $|
|MA|$ MA = {FN \over {TP + FN}} $|
|FA| $ FA = {FP \over {TP + FP}} $|
| MOTA| $MOTA = 1 - {\sum_t(FN + FP + IDs)\over \sum_t gt}$|
|MOTP|$ MOTP = {\sum_{t,i}d_t^i \over \sum_tc_t }$|
|IDP|$ IDP = {IDTP \over {IDTP + IDFP}} $|
|IDR| $ IDR = {IDTP \over {IDTP + IDFN}} $|
|IDF1| $ IDF1 = {2 \over {{1 \over IDP} + {1 \over IDR}}} = {2IDTP \over {2IDTP + IDFP + IDFN}} $|

[Evaluation code](https://github.com/cheind/py-motmetrics)

<a id="markdown-benchmark-results" name="benchmark-results"></a>
## Benchmark Results

<a id="markdown-mot16" name="mot16"></a>
### MOT16

| Rank |      Model       | MOTA |                                                    Paper                                                    | Year |
| :--: | :--------------: | :--: | :---------------------------------------------------------------------------------------------------------: | :--: |
|  1   |     FairMOT      | 68.7 |                                 A Simple Baseline for Multi-Object Tracking                                 | 2020 |
|  2   |       JDE        | 64.4 |                                   Towards Real-Time Multi-Object Tracking                                   | 2019 |
|  3   |      Lif_T       | 61.3 |                     Lifted Disjoint Paths with Application in Multiple Object Tracking                      | 2020 |
|  4   |     MPNTrack     | 58.6 |                            Learning a Neural Solver for Multiple Object Tracking                            | 2020 |
|  5   | DeepMOT-Tracktor | 54.8 |                                 How To Train Your Deep Multi-Object Tracker                                 | 2019 |
|  6   |       TNT        | 49.2 |                      Exploit the Connectivity: Multi-Object Tracking with TrackletNet                       | 2018 |
|  7   |       GCRA       | 48.2 | Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking | 2018 |
|  8   |       FWT        | 47.8 |                      Fusion of Head and Full-Body Detectors for Multi-Object Tracking                       | 2017 |
|  9   |      MOTDT       | 47.6 |   Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification   | 2018 |
|  10  |       NOMT       | 46.4 |                   Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor                   | 2015 |
|  11  |      DMMOT       | 46.1 |                     Online Multi-Object Tracking with Dual Matching Attention Networks                      | 2019 |

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### MOT17

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### MOT20

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## Toolbox

**Tracklab** TrackLab is an easy-to-use modular framework for Multi-Object pose/bbox Tracking that supports many methods, datasets and evaluation metrics.

[Github](https://github.com/TrackingLaboratory/tracklab)

**mmtracking**: OpenMMLab Video Perception Toolbox. It supports Single Object Tracking (SOT), Multiple Object Tracking (MOT), Video Object Detection (VID) with a unified framework.

[Github](https://github.com/open-mmlab/mmtracking)  [DOC](https://mmtracking.readthedocs.io/en/latest/)

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## Course

[link](https://www.youtube.com/watch?v=ay_QLAHcZLY&list=PLadnyz93xCLhSlm2tMYJSKaik39EZV_Uk) is a good course about multiple object tracking. The course is offered as a Massive Open Online Course (MOOC) on edX.

awesome-multiple-object-tracking

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