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
Tracking Road Users using Constraint Programming—0
Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking—0
Tracking with multi-level features—0
Tracking without Label: Unsupervised Multiple Object Tracking via Contrastive Similarity Learning—0
Tracklet Association Tracker: An End-to-End Learning-based Association Approach for Multi-Object Tracking—0
Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking—0
TransFiner: A Full-Scale Refinement Approach for Multiple Object Tracking—0
Type-to-Track: Retrieve Any Object via Prompt-based Tracking—0
S2-Track: A Simple yet Strong Approach for End-to-End 3D Multi-Object Tracking—0
Uncertainty in Minimum Cost Multicuts for Image and Motion Segmentation—0
Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder—0
Unsupervised Multiple Person Tracking using AutoEncoder-Based Lifted Multicuts—0
Using Cross-Domain Detection Loss to Infer Multi-Scale Information for Improved Tiny Head Tracking—0
UTM: A Unified Multiple Object Tracking Model With Identity-Aware Feature Enhancement—0
UTOPIA: Unconstrained Tracking Objects without Preliminary Examination via Cross-Domain Adaptation—0
Video object tracking based on YOLOv7 and DeepSORT—0
Visual Multiple-Object Tracking for Unknown Clutter Rate—0
Walker: Self-supervised Multiple Object Tracking by Walking on Temporal Appearance Graphs—0
Why Accuracy Is Not Enough: The Need for Consistency in Object Detection—0
Z-GMOT: Zero-shot Generic Multiple Object Tracking—0
3D Multiple Object Tracking on Autonomous Driving: A Literature Review—0
A2VIS: Amodal-Aware Approach to Video Instance Segmentation—0
Accurate Object Association and Pose Updating for Semantic SLAM—0
A Continuous, Full-scope, Spatio-temporal Tracking Metric based on KL-divergence—0
A fast multi-object tracking system using an object detector ensemble—0
A Joint Intensity-Neuromorphic Event Imaging System for Resource Constrained Devices—0
A Large Scale Urban Surveillance Video Dataset for Multiple-Object Tracking and Behavior Analysis—0
An Improved End-to-End Multi-Target Tracking Method Based on Transformer Self-Attention—0
An On-line Variational Bayesian Model for Multi-Person Tracking from Cluttered Scenes—0
ArTIST: Autoregressive Trajectory Inpainting and Scoring for Tracking—0
AttMOT: Improving Multiple-Object Tracking by Introducing Auxiliary Pedestrian Attributes—0
A unified neural network for object detection, multiple object tracking and vehicle re-identification—0
Automated learning with a probabilistic programming language: Birch—0
Automated Surgical Skill Assessment in Endoscopic Pituitary Surgery using Real-time Instrument Tracking on a High-fidelity Bench-top Phantom—0
Automatic individual pig detection and tracking in surveillance videos—0
Automatic tracking of protein vesicles—0
BaboonLand Dataset: Tracking Primates in the Wild and Automating Behaviour Recognition from Drone Videos—0
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
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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