SOTAVerified

Object Tracking

Object tracking is the task of taking an initial set of object detections, creating a unique ID for each of the initial detections, and then tracking each of the objects as they move around frames in a video, maintaining the ID assignment. State-of-the-art methods involve fusing data from RGB and event-based cameras to produce more reliable object tracking. CNN-based models using only RGB images as input are also effective. The most popular benchmark is OTB. There are several evaluation metrics specific to object tracking, including HOTA, MOTA, IDF1, and Track-mAP.

( Image credit: Towards-Realtime-MOT )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 751–775 of 1966 papers

TitleStatusHype
Air Signing and Privacy-Preserving Signature Verification for Digital DocumentsCode0
Beyond Traditional Single Object Tracking: A Survey—0
Spatial Cognition: a Wave Hypothesis—0
A Novel Bounding Box Regression Method for Single Object Tracking—0
Learning Correspondence for Deformable Objects—0
MAML MOT: Multiple Object Tracking based on Meta-Learning—0
Common Corruptions for Enhancing and Evaluating Robustness in Air-to-Air Visual Object Detection—0
Ensuring UAV Safety: A Vision-only and Real-time Framework for Collision Avoidance Through Object Detection, Tracking, and Distance Estimation—0
TENet: Targetness Entanglement Incorporating with Multi-Scale Pooling and Mutually-Guided Fusion for RGB-E Object TrackingCode0
DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving—0
Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning—0
3D Extended Object Tracking by Fusing Roadside Sparse Radar Point Clouds and Pixel KeypointsCode0
TeamTrack: A Dataset for Multi-Sport Multi-Object Tracking in Full-pitch Videos—0
360VOTS: Visual Object Tracking and Segmentation in Omnidirectional Videos—0
Inverse Neural Rendering for Explainable Multi-Object Tracking—0
KnotResolver: Tracking self-intersecting filaments in microscopy using directed graphsCode0
MLS-Track: Multilevel Semantic Interaction in RMOT—0
How to deal with glare for improved perception of Autonomous Vehicles—0
Into the Fog: Evaluating Robustness of Multiple Object TrackingCode0
Gaga: Group Any Gaussians via 3D-aware Memory Bank—0
Trashbusters: Deep Learning Approach for Litter Detection and Tracking—0
LRR: Language-Driven Resamplable Continuous Representation against Adversarial Tracking AttacksCode0
Representation Alignment Contrastive Regularization for Multi-Object TrackingCode0
Ego-Motion Aware Target Prediction Module for Robust Multi-Object TrackingCode0
MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark—0
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All datasetsCOESOTFE108QuadTrackSeaDronesSeeBIRDSAI - ICVGIP 2020KITTIMMPTRACKPerception TestVisEvent

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HR-CEUTrack-LargeSuccess Rate65—Unverified
2HR-CEUTrack-BaseSuccess Rate63.2—Unverified
3CEUTrack-LargeSuccess Rate62.8—Unverified
4CEUTrack-BaseSuccess Rate62—Unverified
5SiamR-CNNSuccess Rate60.9—Unverified
6TransTSuccess Rate60.5—Unverified
7SuperDiMPSuccess Rate60.2—Unverified
8TrDiMPSuccess Rate60.1—Unverified
9KeepTrackSuccess Rate59.6—Unverified
10AiATrackSuccess Rate59—Unverified
#ModelMetricClaimedVerifiedStatus
1HR-MonTrack-BaseSuccess Rate68.5—Unverified
2HR-MonTrack-TinySuccess Rate66.3—Unverified
3Multi-modalSuccess Rate63.4—Unverified
4PrDiMPSuccess Rate59—Unverified
5DiMPSuccess Rate57.1—Unverified
6MonTrackSuccess Rate54.9—Unverified
7ATOMSuccess Rate46.5—Unverified
8KYSSuccess Rate26.6—Unverified
#ModelMetricClaimedVerifiedStatus
1OmniTrackHOTA23.45—Unverified
2DeepSORTHOTA21.16—Unverified
3OC-SORTHOTA20.83—Unverified
4ByteTrackHOTA20.66—Unverified
5TrackFormerHOTA19.62—Unverified
6HybridSORTHOTA16.64—Unverified
7DiffMOTHOTA16.4—Unverified
8Bot-SORTHOTA15.77—Unverified
#ModelMetricClaimedVerifiedStatus
1DiMP50Success Rate67.33—Unverified
2PrDiMP50Success Rate67—Unverified
3PrDiMP18Success Rate65.9—Unverified
4DiMP18Success Rate64.6—Unverified
5AtomSuccess Rate63.8—Unverified
#ModelMetricClaimedVerifiedStatus
1finalHumans0.14—Unverified
2night_furyHumans0.05—Unverified
3Yolo based methodHumans0.02—Unverified
4finalHumans0—Unverified
#ModelMetricClaimedVerifiedStatus
1M2-Trackmean precision83.4—Unverified
2BATmean precision75.2—Unverified
#ModelMetricClaimedVerifiedStatus
1UMMT3DMOTA95—Unverified
2MMPTRACK3DMOTA94.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Siam-FCAverage IOU0.66—Unverified
#ModelMetricClaimedVerifiedStatus
1RT-MDNetPrecision Plot0.63—Unverified