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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–175 of 318 papers

TitleStatusHype
BaboonLand Dataset: Tracking Primates in the Wild and Automating Behaviour Recognition from Drone Videos—0
Bayesian nonparametric modeling for predicting dynamic dependencies in multiple object tracking—0
Bi-label Propagation for Generic Multiple Object Tracking—0
Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation—0
Combined Image- and World-Space Tracking in Traffic Scenes—0
ControlLoc: Physical-World Hijacking Attack on Visual Perception in Autonomous Driving—0
Joint Counting, Detection and Re-Identification for Multi-Object Tracking—0
CVPR19 Tracking and Detection Challenge: How crowded can it get?—0
Deep HM-SORT: Enhancing Multi-Object Tracking in Sports with Deep Features, Harmonic Mean, and Expansion IOU—0
Deep Learning in Video Multi-Object Tracking: A Survey—0
DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions—0
Dense Scene Multiple Object Tracking with Box-Plane Matching—0
Depth Perspective-aware Multiple Object Tracking—0
Detection- and Trajectory-Level Exclusion in Multiple Object Tracking—0
DiffMOT: A Real-time Diffusion-based Multiple Object Tracker with Non-linear Prediction—0
Does Video Compression Impact Tracking Accuracy?—0
Efficient Joint Detection and Multiple Object Tracking with Spatially Aware Transformer—0
Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking—0
Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking—0
End-to-end Learning of Multi-sensor 3D Tracking by Detection—0
End-to-end Tracking with a Multi-query Transformer—0
Engineering an Efficient Object Tracker for Non-Linear Motion—0
Enhanced Kalman with Adaptive Appearance Motion SORT for Grounded Generic Multiple Object Tracking—0
Enhancing In-vehicle Multiple Object Tracking Systems with Embeddable Ising Machines—0
Enhancing Multiple Object Tracking Accuracy via Quantum Annealing—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