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

Showing 176–200 of 318 papers

TitleStatusHype
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
Show:102550
← PrevPage 8 of 13Next →

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