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 201–250 of 318 papers

TitleStatusHype
GIAOTracker: A comprehensive framework for MCMOT with global information and optimizing strategies in VisDrone 2021Code0
Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder—0
Does Video Compression Impact Tracking Accuracy?—0
Learning Pixel Trajectories with Multiscale Contrastive Random Walks—0
PatchTrack: Multiple Object Tracking Using Frame Patches—0
LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking—0
Multiple Hypothesis Hypergraph Tracking for Posture Identification in Embryonic Caenorhabditis elegansCode0
Multi-Object Tracking and Segmentation with a Space-Time Memory Network—0
Multiple Object Trackers in OpenCV: A BenchmarkCode0
Extended Object Tracking Using Sets Of Trajectories with a PHD Filter—0
Robust Privacy-Preserving Motion Detection and Object Tracking in Encrypted Streaming Video—0
Semi-TCL: Semi-Supervised Track Contrastive Representation Learning—0
Multiple Object Tracking with Mixture Density Networks for Trajectory Estimation—0
Improving Multiple Object Tracking With Single Object Tracking—0
Online and Real-Time Tracking in a Surveillance Scenario—0
A Joint Intensity-Neuromorphic Event Imaging System for Resource Constrained Devices—0
Soccer Player Tracking in Low Quality VideoCode0
Uncertainty in Minimum Cost Multicuts for Image and Motion Segmentation—0
RelationTrack: Relation-aware Multiple Object Tracking with Decoupled Representation—0
Multiple Object Tracking with Correlation Learning—0
TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking—0
Multi-object Tracking with a Hierarchical Single-branch Network—0
TGCN: Time Domain Graph Convolutional Network for Multiple Objects TrackingCode0
GAKP: GRU Association and Kalman Prediction for Multiple Object Tracking—0
Limitation of Acyclic Oriented Graphs Matching as Cell Tracking Accuracy Measure when Evaluating Mitosis—0
Accurate Object Association and Pose Updating for Semantic SLAM—0
Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking—0
A Deep Learning Bidirectional Temporal Tracking Algorithm for Automated Blood Cell Counting from Non-invasive Capillaroscopy VideosCode0
Learning Local Feature Descriptors for Multiple Object Tracking—0
MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking—0
An Empirical Analysis of Visual Features for Multiple Object Tracking in Urban ScenesCode0
Appearance-free Tripartite Matching for Multiple Object TrackingCode0
Dense Scene Multiple Object Tracking with Box-Plane Matching—0
Unsupervised Spatio-temporal Latent Feature Clustering for Multiple-object Tracking and SegmentationCode0
Learning to associate detections for real-time multiple object tracking—0
ReMOTS: Self-Supervised Refining Multi-Object Tracking and Segmentation—0
IA-MOT: Instance-Aware Multi-Object Tracking with Motion Consistency—0
MOTS: Multiple Object Tracking for General Categories Based On Few-Shot Method—0
Bayesian nonparametric modeling for predicting dynamic dependencies in multiple object tracking—0
ArTIST: Autoregressive Trajectory Inpainting and Scoring for Tracking—0
Supervised and Unsupervised Detections for Multiple Object Tracking in Traffic Scenes: A Comparative StudyCode0
MOT20: A benchmark for multi object tracking in crowded scenesCode0
Tracking Road Users using Constraint Programming—0
Multi-object Tracking via End-to-end Tracklet Searching and Ranking—0
Unsupervised Multiple Person Tracking using AutoEncoder-Based Lifted Multicuts—0
Multiple Object Tracking by Flowing and Fusing—0
Occlusion-Robust Online Multi-Object Visual Tracking using a GM-PHD Filter with CNN-Based Re-Identification—0
Inference for multiple object tracking: A Bayesian nonparametric approach—0
Robust Multi-Modality Multi-Object TrackingCode0
Multiple Object Tracking with Motion and Appearance Cues—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