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 251–300 of 318 papers

TitleStatusHype
LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking—0
MambaTrack: A Simple Baseline for Multiple Object Tracking with State Space Model—0
MAML MOT: Multiple Object Tracking based on Meta-Learning—0
Mixture of Pre-processing Experts Model for Noise Robust Deep Learning on Resource Constrained Platforms—0
MOANA: An Online Learned Adaptive Appearance Model for Robust Multiple Object Tracking in 3D—0
Motion State: A New Benchmark Multiple Object Tracking—0
Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering—0
MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking—0
MotionTrack: End-to-End Transformer-based Multi-Object Tracing with LiDAR-Camera Fusion—0
MotionTrack: Learning Motion Predictor for Multiple Object Tracking—0
MOTSLAM: MOT-assisted monocular dynamic SLAM using single-view depth estimation—0
MOTS: Multi-Object Tracking and Segmentation—0
MOTS: Multiple Object Tracking for General Categories Based On Few-Shot Method—0
DecoderTracker: Decoder-Only Method for Multiple-Object Tracking—0
MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark—0
Multi-Class Multi-Object Tracking using Changing Point Detection—0
Multi-Object Tracking and Segmentation with a Space-Time Memory Network—0
Multi-object Tracking via End-to-end Tracklet Searching and Ranking—0
Multi-object Tracking with a Hierarchical Single-branch Network—0
Multi-object Tracking with Neural Gating Using Bilinear LSTM—0
Multiple Object Tracking: A Literature Review—0
Multiple Object Tracking based on Occlusion-Aware Embedding Consistency Learning—0
Multiple Object Tracking by Flowing and Fusing—0
Multiple-object tracking in cluttered and crowded public spaces—0
Multiple Object Tracking in Recent Times: A Literature Review—0
Multiple Object Tracking in Urban Traffic Scenes with a Multiclass Object Detector—0
Multiple object tracking with context awareness—0
Multiple Object Tracking with Correlation Learning—0
Multiple Object Tracking with Mixture Density Networks for Trajectory Estimation—0
Multiple Object Tracking with Motion and Appearance Cues—0
Multi-Scene Generalized Trajectory Global Graph Solver with Composite Nodes for Multiple Object Tracking—0
Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor—0
NTrack: A Multiple-Object Tracker and Dataset for Infield Cotton Boll Counting—0
Occlusion-Robust Online Multi-Object Visual Tracking using a GM-PHD Filter with CNN-Based Re-Identification—0
Online and Real-Time Tracking in a Surveillance Scenario—0
Online Multi-Object Tracking via Structural Constraint Event Aggregation—0
OST: Efficient One-stream Network for 3D Single Object Tracking in Point Clouds—0
Overlap Suppression Clustering for Offline Multi-Camera People Tracking—0
PatchTrack: Multiple Object Tracking Using Frame Patches—0
PathTrack: Fast Trajectory Annotation with Path Supervision—0
POI: Multiple Object Tracking with High Performance Detection and Appearance Feature—0
Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking—0
RelationTrack: Relation-aware Multiple Object Tracking with Decoupled Representation—0
ReMOTS: Self-Supervised Refining Multi-Object Tracking and Segmentation—0
Robust Online Multi-target Visual Tracking using a HISP Filter with Discriminative Deep Appearance Learning—0
Robust Privacy-Preserving Motion Detection and Object Tracking in Encrypted Streaming Video—0
S^3Track: Self-supervised Tracking with Soft Assignment Flow—0
Samba: Synchronized Set-of-Sequences Modeling for Multiple Object Tracking—0
Semi-TCL: Semi-Supervised Track Contrastive Representation Learning—0
Significant changes in EEG neural oscillations during different phases of three-dimensional multiple object tracking task (3D-MOT) imply different roles for attention and working memory—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