多目标跟踪MOT相关论文和代码资源列表

前言

这段时间在整理毕设,所以这里结合了SpyderXu分享的内容把多目标跟踪相关的文献资源共享一下,由于文章很多,所以我这里只整理3年以内的,对于年限久远的,这里只取提供了代码的和比较经典的。并且尽可能注释了相关算法在MOT数据集上的名称。各自算法的性能比较可以看论文以及MOT官网。以下内容我都同步到了我的知乎和github

在线跟踪(Online)

Name Source Publication Notes
DeepMOT:A Differentiable Framework for Training Multiple Object Trackers [pdf] [code] arXiv(2019) 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
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
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

离线跟踪(Batch)

Name Source Publication Notes
Learning non-uniform hypergraph for multi-object tracking [pdf] AAAI2019 NT
Learning a Neural Solver for Multiple Object Tracking [pdf] [code] arXiv(2019) 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)
Deep affinity network for multiple object tracking [pdf] [code] PAMI(2019) DAN
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
Learning to Divide and Conquer for Online Multi-Target Tracking [pdf] [code] ICCV2015 LDCT
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 -

跨摄像头跟踪(MTMC)

Name Source Publication Notes
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&多模态跟踪

Name Source Publication Notes
Robust Multi-Modality Multi-Object Tracking [pdf] [code] ICCV2019 mmMOT
A baseline for 3D Multi-Object Tracking [pdf] [code] arXiv -

综述

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

数据集

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。

评价体系

ClearMOT

IDF1

Code: python、matlab

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