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

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Showing 551–600 of 1966 papers

TitleStatusHype
Mobile Vision Transformer-based Visual Object TrackingCode1
DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions—0
Separable Self and Mixed Attention Transformers for Efficient Object TrackingCode1
Fast and Resource-Efficient Object Tracking on Edge Devices: A Measurement StudyCode1
FishMOT: A Simple and Effective Method for Fish Tracking Based on IoU MatchingCode1
Robust Visual Tracking by Motion Analyzing—0
Efficient Training for Visual Tracking with Deformable Transformer—0
Towards Efficient Training with Negative Samples in Visual Tracking—0
BEVTrack: A Simple and Strong Baseline for 3D Single Object Tracking in Bird's-Eye ViewCode1
UnsMOT: Unified Framework for Unsupervised Multi-Object Tracking with Geometric Topology Guidance—0
Consensus-based Distributed Variational Multi-object Tracker in Multi-Sensor Network—0
Object-Centric Multiple Object TrackingCode1
RGB-T Tracking via Multi-Modal Mutual Prompt LearningCode1
Improving Underwater Visual Tracking With a Large Scale Dataset and Image EnhancementCode1
Occlusion-Aware Detection and Re-ID Calibrated Network for Multi-Object Tracking—0
The Interstate-24 3D Dataset: a new benchmark for 3D multi-camera vehicle tracking—0
1st Place Solution for the 5th LSVOS Challenge: Video Instance SegmentationCode1
End-to-end Autonomous Driving using Deep Learning: A Systematic Review—0
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and SegmentationCode1
ReST: A Reconfigurable Spatial-Temporal Graph Model for Multi-Camera Multi-Object TrackingCode1
Distributed Extended Object Tracking Using Coupled Velocity Model from WLS Perspective—0
Synchronize Feature Extracting and Matching: A Single Branch Framework for 3D Object Tracking—0
Motion-to-Matching: A Mixed Paradigm for 3D Single Object TrackingCode1
RefEgo: Referring Expression Comprehension Dataset from First-Person Perception of Ego4DCode1
Segment Anything for Microscopy—0
Delving into Motion-Aware Matching for Monocular 3D Object TrackingCode1
TrackFlow: Multi-Object Tracking with Normalizing Flows—0
TOPIC: A Parallel Association Paradigm for Multi-Object Tracking under Complex Motions and Diverse ScenesCode2
BackTrack: Robust template update via Backward Tracking of candidate template—0
DiffusionTrack: Diffusion Model For Multi-Object TrackingCode2
LEGO: Learning and Graph-Optimized Modular Tracker for Online Multi-Object Tracking with Point Clouds—0
BOTT: Box Only Transformer Tracker for 3D Object Tracking—0
AttMOT: Improving Multiple-Object Tracking by Introducing Auxiliary Pedestrian Attributes—0
FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow—0
HHTrack: Hyperspectral Object Tracking Using Hybrid Attention—0
A One Stop 3D Target Reconstruction and multilevel Segmentation MethodCode1
3DMOTFormer: Graph Transformer for Online 3D Multi-Object TrackingCode1
Collaborative Tracking Learning for Frame-Rate-Insensitive Multi-Object TrackingCode0
Seed Kernel Counting using Domain Randomization and Object Tracking Neural Networks—0
Tracking Players in a Badminton Court by Two Cameras—0
An End-to-End Framework of Road User Detection, Tracking, and Prediction from Monocular Images—0
OmniDataComposer: A Unified Data Structure for Multimodal Data Fusion and Infinite Data GenerationCode1
Nerve Block Target Localization and Needle Guidance for Autonomous Robotic Ultrasound Guided Regional Anesthesia—0
InterTracker: Discovering and Tracking General Objects Interacting with Hands in the Wild—0
Enhancing Cell Tracking with a Time-Symmetric Deep Learning ApproachCode0
ReIDTrack: Multi-Object Track and Segmentation Without Motion—0
Learning Spatial Distribution of Long-Term Trackers Scores—0
A Hybrid Approach To Real-Time Multi-Object Tracking—0
MonoNext: A 3D Monocular Object Detection with ConvNext—0
Hybrid-SORT: Weak Cues Matter for Online Multi-Object TrackingCode2
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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