Refer to SpyderXu with some supplements

Online

| Name | Source | Publication | Notes | | :----------------------------------------------------------: | :----------------------------------------------------------: | :---------: | :----: | | Simultaneous Detection and Tracking with Motion Modelling for Multiple Object Tracking | [pdf]| ECCV2020| - | | Towards Real-Time Multi-Object Tracking | [pdf] [code]| ECCV2020 | - | | Tracking Objects as Points | [pdf] [code]| ECCV 2020 | - | | Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking | [pdf]| ECCV2020| - | | Segment as Points for Efficient Online Multi-Object Tracking and Segmentation | [pdf] [code] | ECCV2020 oral | mots| | MAT: Motion-Aware Multi-Object Tracking | [pdf] | arXiv | 2020.9.18 | | Adopting Tubes to Track Multi-Object in a One-Step Training Model | [pdf] [code] | CVPR2020 | TubeTK | | Joint Detection and Multi-Object Tracking with Graph Neural Networks | [pdf] | arxiv(2020) | JDMOTGNN | | Graph Networks for Multiple Object Tracking | [pdf] [code] | WACV2020 | GNMOT | | Deep association: End-to-end graph-based learning for multiple object tracking with conv-graph neural network | [pdf] | ICMR2019 | DAN | | SQE: a Self Quality Evaluation Metric for Parameters Optimization in Multi-Object Tracking | [pdf] | arxiv(2020) | SQE | | Autoregressive Trajectory Inpainting and Scoring for Tracking | [pdf] | CVPR2020 | ArTIST | | Multiple Object Tracking with Siamese Track-RCNN | [pdf] | arxiv(2020) | Siamese Track-RCNN | | Online Single Stage Joint Detection and Tracking | [pdf] | CVPR2020 | RetinaTrack | | A Simple Baseline for Multi-Object Tracking | [pdf][code] | arXiv(2019) | FairMOT | | Tracking Objects as Points | [pdf] [code] | arXiv(2019) | CenterTrack | | Refinements in Motion and Appearance for Online Multi-Object Tracking | [pdf] [code] | arXiv(2019) | MIFT | | Multiple Object Tracking by Flowing and Fusing | [pdf] | arXiv(2019) | FFT | | A Unified Object Motion and Affinity Model for Online Multi-Object Tracking | [pdf][code] | CVPR2020 | UMA | | DeepMOT:A Differentiable Framework for Training Multiple Object Trackers | [pdf] [code] | CVPR2020 | DeepMOT | | Online multiple pedestrian tracking using deep temporal appearance matching association | [pdf] [code] | arXiv(2019) | DDTAMA19 | | Spatial-temporal relation networks for multi-object tracking | [pdf] | ICCV2019 | STRN | | Towards Real-Time Multi-Object Tracking | [pdf] [code] | arXiv(2019) | JDE(private) | | Multi-object tracking with multiple cues and switcher-aware classification | [pdf] | arXiv(2019) | LSST | | FAMNet: Joint learning of feature, affinity and multi-dimensional assignment for online multiple object tracking | [pdf] | ICCV2019 | FAMNet | | Online multi-object tracking with instance-aware tracker and dynamic model refreshment | [pdf] | WACV2019 | KCF | | Tracking without bells and whistles | [pdf] [code] | ICCV2019 | Tracktor | | MOTS: Multi-Object Tracking and Segmentation | [pdf] [code] | CVPR2019 | Track R-CNN | | Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking | [pdf] [code] | CVPR2019 | SASMOT17 | | Deep affinity network for multiple object tracking | [pdf] [code] | PAMI(2019) | DAN | | Recurrent autoregressive networks for online multi-object tracking | [pdf] | WACV2018 | RAN | | Real-time multiple people tracking with deeply learned candidate selection and person re-identification | [[pdf]](https://www.researchgate.net/publication/326224594Real-timeMultiplePeopleTrackingwithDeeplyLearnedCandidateSelectionandPersonRe-identification) [code] | ICME2018 | MOTDT | | Online multi-object tracking with dual matching attention networks | [[pdf]](http://openaccess.thecvf.com/contentECCV2018/papers/JiZhuOnlineMulti-ObjectTrackingECCV2018paper.pdf) [code] | ECCV2018 | DMAN | | Extending IOU Based Multi-Object Tracking by Visual Information | [pdf] [code] | AVSS2018 | V-IOU | | Online Multi-target Tracking using Recurrent Neural Networks | [pdf] [code] | AAAI2017 | MOT-RNN | | Detect to Track and Track to Detect | [pdf] [code] | ICCV2017 | D&T(private) | | Online multi-object tracking using CNN-based single object tracker with spatial-temporal attention mechanism | [pdf] | ICCV2017 | STAM | | Tracking the untrackable: Learning to track multiple cues with long-term dependencies | [pdf] | ICCV2017 | AMIR | | Simple online and realtime tracking with a deep association metric | [pdf] [code] | ICIP2017 | DeepSort | | High-speed tracking-by-detection without using image information | [pdf] [code] | AVSS2017 | IOU Tracker | | Simple online and realtime tracking | [pdf] [code] | ICIP2016 | Sort | | Temporal dynamic appearance modeling for online multi-person tracking | [pdf] | CVIU(2016) | TDAM | | Online multi-object tracking via structural constraint event aggregation | [pdf] | CVPR2016 | SCEA | | Online Multi-Object Tracking Via Robust Collaborative Model and Sample Selection | [pdf] [code] | CVIU2016 | RCMSS | | Learning to Track: Online Multi-Object Tracking by Decision Making | [pdf] [code] | ICCV2015 | MDP | | Learning to Divide and Conquer for Online Multi-Target Tracking | [pdf] [code] | ICCV2015 | LDCT | | Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning | [pdf] [code] | CVPR2014 | CMOT | | The Way They Move: Tracking Targets with Similar Appearance | [pdf] [code] | ICCV2013 | SMOT | | Online Multi-Person Tracking by Tracker Hierarchy | [pdf] [code] | AVSS2012 | OMPTTH |

Batch

| Name | Source | Publication | Notes | | :----------------------------------------------------------: | :----------------------------------------------------------: | :---------: | :---: | | Lifted Disjoint Paths with Application in Multiple Object Tracking | [pdf] [code] | ICML2020 | LifT | | Learning non-uniform hypergraph for multi-object tracking | [pdf] | AAAI2019 | NT | | Learning a Neural Solver for Multiple Object Tracking | [pdf] [code] | CVPR2020 | MPNTracker | | Deep learning of graph matching | [[pdf]](http://openaccess.thecvf.com/contentcvpr2018/papers/ZanfirDeepLearningofCVPR2018paper.pdf) | CVPR2018 | 深度图匹配 | | muSSP: Efficient Min-cost Flow Algorithm for Multi-object Tracking | [pdf] [code] | NIPS(2019) | muSSP | | Exploit the connectivity: Multi-object tracking with trackletnet | [pdf] [code] | ACM mm 2019 | TNT(eTC) | | Multiple people tracking using body and joint detections | [[pdf]](http://openaccess.thecvf.com/contentCVPRW2019/papers/BMTT/HenschelMultiplePeopleTrackingUsingBodyandJointDetectionsCVPRW2019paper.pdf) | CVPRW2019 | JBNOT | | Aggregate Tracklet Appearance Features for Multi-Object Tracking | [pdf] | SPL(2019) | NOTA | | Customized multi-person tracker | [pdf] | ACCV2018 | HCC | | Multi-object tracking with neural gating using bilinear lstm | [pdf] | ECCV2018 | MHTbLSTM | | Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking | [pdf] | ICME2018 | GCRE | | Multiple People Tracking with Lifted Multicut and Person Re-identification | [[pdf]](http://openaccess.thecvf.com/contentcvpr2017/papers/TangMultiplePeopleTrackingCVPR2017paper.pdf) | CVPR2017 | LMP | | Deep network flow for multi-object tracking | [[pdf]](http://openaccess.thecvf.com/contentcvpr2017/papers/SchulterDeepNetworkFlowCVPR2017paper.pdf) | CVPR2017 | - | | Non-markovian globally consistent multi-object tracking | [[pdf]](http://openaccess.thecvf.com/contentICCV2017/papers/MaksaiNon-MarkovianGloballyConsistentICCV2017paper.pdf) [[code]](https://github.com/maksay/ptrackcpp) | ICCV2017 | - | | Multi-Object Tracking with Quadruplet Convolutional Neural Networks | [pdf] | CVPR2017 | Quad-CNN | | Enhancing detection model for multiple hypothesis tracking | [pdf] | CVPRW2017 | EDMT | | POI: Multiple Object Tracking with High Performance Detection and Appearance Feature | [pdf] | ECCV2016 | KNDT | | Multiple hypothesis tracking revisited | [pdf] [code] | ICCV2015 | MHT-DAM | | Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor | [pdf] | ICCV2015 | NOMT | | On Pairwise Costs for Network Flow Multi-Object Tracking | [pdf] [code] | CVPR2015 | - | | Multiple Target Tracking Based on Undirected Hierarchical Relation Hypergraph | [pdf] [code] | CVPR2014 | H2T | | Continuous Energy Minimization for Multi-Target Tracking | [pdf] [code] | CVPR2014 | CEM | | GMCP-Tracker: Global Multi-object Tracking Using Generalized Minimum Clique Graphs | [pdf] [code] | ECCV2012 | GMCP | | Multiple Object Tracking using K-Shortest Paths Optimization | [pdf] [code] | PAMI2011 | KSP | | Global data association for multi-object tracking using network flows | [pdf] [code] | CVPR2008 | - |

MTMC

| Name | Source | Publication | Notes | | :----------------------------------------------------------: | :----------------------------------------------------------: | :---------------: | :-------------------: | | Locality Aware Appearance Metric for Multi-Target Multi-Camera Tracking | [pdf] code | CVPR2019 Workshop | LAAM | | CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification | [pdf] | CVPR2019 | CityFlow | | Features for multi-target multi-camera tracking and re-identification | [pdf] [code] | CVPR2018 | DeepCC(MTMC) | | Rolling Shutter and Radial Distortion Are Features for High Frame Rate Multi-Camera Tracking | [pdf] | CVPR2018 | - | | Towards a Principled Integration of Multi-Camera Re-Identification andTracking through Optimal Bayes Filters | [pdf] [code] | CVPR2017 | towards-reid-tracking |

3D&Multi-Modality

| Name | Source | Publication | Notes | | :----------------------------------------------------------: | :----------------------------------------------------------: | :---------: | :------------: | | Joint 3D Tracking and Forecasting with Graph Neural Network and Diversity Sampling | [pdf] [code] | arxiv | GNNTrkForecast | | Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning | [pdf] [code] | CVPR2020 | GNN3DMOT | | Robust Multi-Modality Multi-Object Tracking | [pdf] [code] | ICCV2019 | mmMOT | | A baseline for 3D Multi-Object Tracking | [pdf] [code] | arXiv | - | | 3D Object Detection and Tracking Based on Streaming Data | [pdf] | ICRA2020 | DODT | | Factor Graph based 3D Multi-Object Tracking in Point Clouds | [pdf] [video]| IROS2020 | DODT | | DeepTracking-Net: 3D Tracking with Unsupervised Learning of Continuous Flow | [pdf] | arXiv | 2020.6.24 | | Center-based 3D Object Detection and Tracking | [pdf] [code]| arXiv | 2020.6.19 | | 1st Place Solutions for Waymo Open Dataset Challenges -- 2D and 3D Tracking | [pdf]| arXiv| technical report|

Review

Multiple Object Tracking: A Literature Review

Machine Learning Methods for Solving Assignment Problems in Multi-Target Tracking

Deep Learning in Video Multi-Object Tracking_ A Survey

Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects

Datasets

MOT：包含2D MOT2015、3D MOT2015、MOT16、MOT17和MOT17Det等多个子数据集，提供了ACF、DPM、Faster RCNN、SDP等多个检测器输入。包含不同的相机视角、相机运动、场景和时间变化以及密集场景。

KITTI：提供了汽车和行人的标注，场景较稀疏。

TUD Stadtmitte：包含3D人体姿态识别、多视角行人检测和朝向检测、以及行人跟踪的标注，相机视角很低，数据集不大。

ETHZ：由手机拍摄的多人跟踪数据集，包含三个场景。

EPFL：多摄像头采集的行人检测和跟踪数据集，每隔摄像头离地2米，实验人员就是一个实验室的，分为实验室、校园、平台、通道、篮球场这5个场景，每个场景下都有多个摄像头，每个摄像头拍摄2分钟左右。

KIT AIS：空中拍摄的，只有行人的头

PETS：比较早期的视频，有各式各样的行人运动。

DukeMTMC：多摄像头多行人跟踪。

MOTS：多目标跟踪与分割。

Evaluation

ClearMOT

IDF1

Code: python、matlab