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 151–200 of 318 papers

TitleStatusHype
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
Estimating Dynamic Flow Features in Groups of Tracked Objects—0
Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model—0
Exploiting Temporal Relations on Radar Perception for Autonomous Driving—0
MambaMOT: State-Space Model as Motion Predictor for Multi-Object Tracking—0
Extended Object Tracking Using Sets Of Trajectories with a PHD Filter—0
FACT: Feature Adaptive Continual-learning Tracker for Multiple Object Tracking—0
FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking—0
FeatureSORT: Essential Features for Effective Tracking—0
Focus On Details: Online Multi-object Tracking with Diverse Fine-grained Representation—0
FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation—0
FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow—0
Forensic Video Analytic Software—0
Frame-wise Motion and Appearance for Real-time Multiple Object Tracking—0
GAKP: GRU Association and Kalman Prediction for Multiple Object Tracking—0
Generalizing Multiple Object Tracking to Unseen Domains by Introducing Natural Language Representation—0
GMMCP Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking—0
GSLAMOT: A Tracklet and Query Graph-based Simultaneous Locating, Mapping, and Multiple Object Tracking System—0
Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads—0
IA-MOT: Instance-Aware Multi-Object Tracking with Motion Consistency—0
Improving Multiple Object Tracking with Optical Flow and Edge Preprocessing—0
Improving Multiple Object Tracking With Single Object Tracking—0
Deep Similarity Metric Learning for Real-Time Pedestrian Tracking—0
Improving tracking with a tracklet associator—0
Inference for multiple object tracking: A Bayesian nonparametric approach—0
Instance Flow Based Online Multiple Object Tracking—0
INTACT: Inducing Noise Tolerance through Adversarial Curriculum Training for LiDAR-based Safety-Critical Perception and Autonomy—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