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
A unified neural network for object detection, multiple object tracking and vehicle re-identification—0
How To Train Your Deep Multi-Object TrackerCode0
CVPR19 Tracking and Detection Challenge: How crowded can it get?—0
Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking—0
Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object TrackingCode1
Tracking in Urban Traffic Scenes from Background Subtraction and Object Detection—0
Frame-wise Motion and Appearance for Real-time Multiple Object Tracking—0
Mixture of Pre-processing Experts Model for Noise Robust Deep Learning on Resource Constrained Platforms—0
A Large Scale Urban Surveillance Video Dataset for Multiple-Object Tracking and Behavior Analysis—0
Spatial-Temporal Relation Networks for Multi-Object Tracking—0
FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking—0
MOTS: Multi-Object Tracking and Segmentation—0
MOANA: An Online Learned Adaptive Appearance Model for Robust Multiple Object Tracking in 3D—0
Intelligent Intersection: Two-Stream Convolutional Networks for Real-time Near Accident Detection in Traffic Video—0
Automatic individual pig detection and tracking in surveillance videos—0
Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking—0
Joint Monocular 3D Vehicle Detection and TrackingCode0
Deep Affinity Network for Multiple Object TrackingCode0
Automated learning with a probabilistic programming language: Birch—0
Combined Image- and World-Space Tracking in Traffic Scenes—0
Multiple Object Tracking in Urban Traffic Scenes with a Multiclass Object Detector—0
Multi-object Tracking with Neural Gating Using Bilinear LSTM—0
Tracklet Association Tracker: An End-to-End Learning-based Association Approach for Multi-Object Tracking—0
Learning The Sequential Temporal Information with Recurrent Neural Networks—0
A Region-based Gauss-Newton Approach to Real-Time Monocular Multiple Object TrackingCode0
End-to-end Learning of Multi-sensor 3D Tracking by Detection—0
Deep Similarity Metric Learning for Real-Time Pedestrian Tracking—0
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
A Continuous, Full-scope, Spatio-temporal Tracking Metric based on KL-divergence—0
State-Space Abstractions for Probabilistic Inference: A Systematic Review—0
Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking—0
The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking—0
Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering—0
Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object TrackingCode0
Tracking Multiple Moving Objects Using Unscented Kalman Filtering Techniques—0
Improving Multiple Object Tracking with Optical Flow and Edge Preprocessing—0
Psychlab: A Psychology Laboratory for Deep Reinforcement Learning AgentsCode0
SOT for MOT—0
Real-Time Multiple Object Tracking - A Study on the Importance of SpeedCode0
Joint Graph Decomposition & Node Labeling: Problem, Algorithms, Applications—0
Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking—0
Lucid Data Dreaming for Video Object SegmentationCode0
Simple Online and Realtime Tracking with a Deep Association MetricCode1
PathTrack: Fast Trajectory Annotation with Path Supervision—0
Instance Flow Based Online Multiple Object Tracking—0
Visual Multiple-Object Tracking for Unknown Clutter Rate—0
Joint Graph Decomposition and Node Labeling: Problem, Algorithms, ApplicationsCode0
Multiple Object Tracking with Kernelized Correlation Filters in Urban Mixed TrafficCode0
POI: Multiple Object Tracking with High Performance Detection and Appearance Feature—0
Multi-Class Multi-Object Tracking using Changing Point Detection—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