SOTAVerified

Multiple Object Tracking

Multiple Object Tracking is the problem of automatically identifying multiple objects in a video and representing them as a set of trajectories with high accuracy.

Source: SOT for MOT

Papers

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Showing 51–100 of 318 papers

TitleStatusHype
RobMOT: Robust 3D Multi-Object Tracking by Observational Noise and State Estimation Drift Mitigation on LiDAR PointCloudCode0
MAML MOT: Multiple Object Tracking based on Meta-Learning—0
Into the Fog: Evaluating Robustness of Multiple Object TrackingCode0
BoostTrack: boosting the similarity measure and detection confidence for improved multiple object trackingCode3
MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark—0
Enhancing Multiple Object Tracking Accuracy via Quantum Annealing—0
Multiple Object Tracking as ID PredictionCode3
PNAS-MOT: Multi-Modal Object Tracking with Pareto Neural Architecture SearchCode1
MambaMOT: State-Space Model as Motion Predictor for Multi-Object Tracking—0
Learning Data Association for Multi-Object Tracking using Only CoordinatesCode1
Delving into the Trajectory Long-tail Distribution for Muti-object TrackingCode2
DiffMOT: A Real-time Diffusion-based Multiple Object Tracker with Non-linear Prediction—0
Beyond Kalman Filters: Deep Learning-Based Filters for Improved Object TrackingCode0
Motion State: A New Benchmark Multiple Object Tracking—0
NTrack: A Multiple-Object Tracker and Dataset for Infield Cotton Boll Counting—0
Multi-Scene Generalized Trajectory Global Graph Solver with Composite Nodes for Multiple Object Tracking—0
UCMCTrack: Multi-Object Tracking with Uniform Camera Motion CompensationCode2
Adaptive Confidence Threshold for ByteTrack in Multi-Object TrackingCode0
Multiple Toddler Tracking in Indoor VideosCode0
Contrastive Learning for Multi-Object Tracking with TransformersCode0
Multiple Object Tracking based on Occlusion-Aware Embedding Consistency Learning—0
DecoderTracker: Decoder-Only Method for Multiple-Object Tracking—0
COOLer: Class-Incremental Learning for Appearance-Based Multiple Object TrackingCode0
DARTH: Holistic Test-time Adaptation for Multiple Object TrackingCode1
3D Multiple Object Tracking on Autonomous Driving: A Literature Review—0
RaTrack: Moving Object Detection and Tracking with 4D Radar Point CloudCode2
Forensic Video Analytic Software—0
SoccerNet 2023 Challenges ResultsCode1
DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions—0
FishMOT: A Simple and Effective Method for Fish Tracking Based on IoU MatchingCode1
Object-Centric Multiple Object TrackingCode1
LEGO: Learning and Graph-Optimized Modular Tracker for Online Multi-Object Tracking with Point Clouds—0
AttMOT: Improving Multiple-Object Tracking by Introducing Auxiliary Pedestrian Attributes—0
FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow—0
Hybrid-SORT: Weak Cues Matter for Online Multi-Object TrackingCode2
MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object TrackingCode2
MotionTrack: End-to-End Transformer-based Multi-Object Tracing with LiDAR-Camera Fusion—0
Segmentation and Tracking of Vegetable Plants by Exploiting Vegetable Shape Feature for Precision Spray of Agricultural RobotsCode1
Iterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in SportsCode1
UTOPIA: Unconstrained Tracking Objects without Preliminary Examination via Cross-Domain Adaptation—0
Tracking Objects with 3D Representation from Videos—0
SparseTrack: Multi-Object Tracking by Performing Scene Decomposition based on Pseudo-DepthCode1
MotionTrack: Learning Motion Predictor for Multiple Object Tracking—0
Z-GMOT: Zero-shot Generic Multiple Object Tracking—0
Linear Object Detection in Document Images using Multiple Object Tracking—0
Type-to-Track: Retrieve Any Object via Prompt-based Tracking—0
S^3Track: Self-supervised Tracking with Soft Assignment Flow—0
A Feasibility Study on Indoor Localization and Multi-person Tracking Using Sparsely Distributed Camera Network with Edge ComputingCode0
OVTrack: Open-Vocabulary Multiple Object TrackingCode1
Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads—0
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All datasetsKITTI Test (Online Methods)BDD100K valBDD100K testCroHDSportsMOTKITTI Test (Offline Methods)RADIATEWaymo Open DatasetGMOT-40UA-DETRACYouTube-Hands

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SRK ODESAMOTA90.03—Unverified
2MOSTFusionMOTA84.83—Unverified
3mmMOT-normalMOTA84.77—Unverified
43DTMOTA84.52—Unverified
5Mono3DTMOTA84.52—Unverified
6RRC-IIITHMOTA84.24—Unverified
7RobMOT (Dynamic)HOTA81.8—Unverified
8RobMOTHOTA81.76—Unverified
9MCTrackHOTA81.07—Unverified
10KFDLHOTA81.06—Unverified
#ModelMetricClaimedVerifiedStatus
1ByteTrackmMOTA45.5—Unverified
2UNINEXT-HmMOTA44.2—Unverified
3MOTRv2mMOTA43.6—Unverified
4QDTrackmMOTA42.1—Unverified
5ContrasTRmMOTA41.7—Unverified
6UnicornmMOTA41.2—Unverified
7TETermMOTA39.1—Unverified
8QDTrackmMOTA36.6—Unverified
9Adaptive-searching -windows TrackermMOTA34.4—Unverified
#ModelMetricClaimedVerifiedStatus
1ContrasTRmMOTA42.8—Unverified
2SUSHImMOTA40.2—Unverified
3ByteTrackmMOTA40.1—Unverified
4QDtrackmMOTA35.6—Unverified
5Yu et al.mMOTA26.3—Unverified
#ModelMetricClaimedVerifiedStatus
1PP-TrackingMOTA72.6—Unverified
2OC-SORTMOTA67.9—Unverified
3HeadHunter-TMOTA63.6—Unverified
4TracktorMOTA58.9—Unverified
5SORTMOTA46.4—Unverified
#ModelMetricClaimedVerifiedStatus
1MOTIP (Deformable DETR, with SportsMOT val)HOTA75.2—Unverified
2TrackSSMHOTA74.4—Unverified
3MOTIP (Deformable DETR)HOTA71.9—Unverified
#ModelMetricClaimedVerifiedStatus
1MCTrackHOTA82.75—Unverified
2BiTrackHOTA82.7—Unverified
#ModelMetricClaimedVerifiedStatus
1SIRAMOTA47.79—Unverified
2TempoRadarMOTA37.91—Unverified
#ModelMetricClaimedVerifiedStatus
1QDTrackMOTA55.6—Unverified
2RetinaTrackMOTA44.92—Unverified
#ModelMetricClaimedVerifiedStatus
1MAC-SORTHOTA58.58—Unverified
#ModelMetricClaimedVerifiedStatus
1EB & TADNMOTA23.7—Unverified
#ModelMetricClaimedVerifiedStatus
1HandLerMOTA70—Unverified