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 551–575 of 1966 papers

TitleStatusHype
Beyond Traditional Single Object Tracking: A Survey—0
DIMBA: Discretely Masked Black-Box Attack in Single Object Tracking—0
Beyond the Pixels: Exploring the Effects of Bit-Level Network and File Corruptions on Video Model Robustness—0
A Connectome Based Hexagonal Lattice Convolutional Network Model of the Drosophila Visual System—0
FlowTrack: Point-level Flow Network for 3D Single Object Tracking—0
Beyond standard benchmarks: Parameterizing performance evaluation in visual object tracking—0
AM-SORT: Adaptable Motion Predictor with Historical Trajectory Embedding for Multi-Object Tracking—0
DiffMOT: A Real-time Diffusion-based Multiple Object Tracker with Non-linear Prediction—0
Diff-MM: Exploring Pre-trained Text-to-Image Generation Model for Unified Multi-modal Object Tracking—0
Beyond SOT: Tracking Multiple Generic Objects at Once—0
Flow Guided Short-term Trackers with Cascade Detection for Long-term Tracking—0
Dictionary Learning under Symmetries via Group Representations—0
3D Multi-Object Tracking with Differentiable Pose Estimation—0
DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception—0
DGD: Dynamic 3D Gaussians Distillation—0
FlowMOT: 3D Multi-Object Tracking by Scene Flow Association—0
Development and evaluation of automated localisation and reconstruction of all fruits on tomato plants in a greenhouse based on multi-view perception and 3D multi-object tracking—0
A Comprehensive Study of Object Tracking in Low-Light Environments—0
DeTrack: In-model Latent Denoising Learning for Visual Object Tracking—0
DetFlowTrack: 3D Multi-object Tracking based on Simultaneous Optimization of Object Detection and Scene Flow Estimation—0
F&F Attack: Adversarial Attack against Multiple Object Trackers by Inducing False Negatives and False Positives—0
Beyond Boxes: Mask-Guided Spatio-Temporal Feature Aggregation for Video Object Detection—0
A low-power end-to-end hybrid neuromorphic framework for surveillance applications—0
Detection- and Trajectory-Level Exclusion in Multiple Object Tracking—0
Detection and Tracking of Small Objects in Sparse 3D Laser Range Data—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