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 201–225 of 318 papers

TitleStatusHype
INTACT: Inducing Noise Tolerance through Adversarial Curriculum Training for LiDAR-based Safety-Critical Perception and Autonomy—0
Intelligent Intersection: Two-Stream Convolutional Networks for Real-time Near Accident Detection in Traffic Video—0
Interactive Multi-scale Fusion of 2D and 3D Features for Multi-object Tracking—0
Is Multiple Object Tracking a Matter of Specialization?—0
Joint Graph Decomposition & Node Labeling: Problem, Algorithms, Applications—0
Learning Local Feature Descriptors for Multiple Object Tracking—0
Learning Pixel Trajectories with Multiscale Contrastive Random Walks—0
Learning The Sequential Temporal Information with Recurrent Neural Networks—0
Learning to associate detections for real-time multiple object tracking—0
Learning to Track: Online Multi-Object Tracking by Decision Making—0
LEGO: Learning and Graph-Optimized Modular Tracker for Online Multi-Object Tracking with Point Clouds—0
LiDAR MOT-DETR: A LiDAR-based Two-Stage Transformer for 3D Multiple Object Tracking—0
Limitation of Acyclic Oriented Graphs Matching as Cell Tracking Accuracy Measure when Evaluating Mitosis—0
Linear Object Detection in Document Images using Multiple Object Tracking—0
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
MOT20: A benchmark for multi object tracking in crowded scenes—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
Show:102550
← PrevPage 9 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