Refer to SpyderXu with some supplements
| 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) | JDMOT_GNN |
| 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) | DD_TAMA19 |
| 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 | SAS_MOT17 |
| 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] [code] | ICME2018 | MOTDT |
| Online multi-object tracking with dual matching attention networks | [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 |
| Name | Source | Publication | Notes |
|---|---|---|---|
| Lifted Disjoint Paths with Application in Multiple Object Tracking | [pdf] [code] | ICML2020 | Lif_T |
| 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] | 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] | 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 | MHT_bLSTM |
| 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] | CVPR2017 | LMP |
| Deep network flow for multi-object tracking | [pdf] | CVPR2017 | - |
| Non-markovian globally consistent multi-object tracking | [pdf] [code] | 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 | - |
| 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 |
| 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 |
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
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。
