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 1101–1150 of 1966 papers

TitleStatusHype
Deep HM-SORT: Enhancing Multi-Object Tracking in Sports with Deep Features, Harmonic Mean, and Expansion IOU—0
Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey—0
Deep learning based 2.5D flow field estimation for maximum intensity projections of 4D optical coherence tomography—0
Deep Learning-Based Multi-Object Tracking: A Comprehensive Survey from Foundations to State-of-the-Art—0
Deep Learning-Based Robust Multi-Object Tracking via Fusion of mmWave Radar and Camera Sensors—0
Deep Learning for UAV-based Object Detection and Tracking: A Survey—0
Deep Learning in Video Multi-Object Tracking: A Survey—0
Deep Learning Method for Cell-Wise Object Tracking, Velocity Estimation and Projection of Sensor Data over Time—0
Deep Learning of Appearance Models for Online Object Tracking—0
Deep Learning Techniques for Video Instance Segmentation: A Survey—0
Deep LG-Track: An Enhanced Localization-Confidence-Guided Multi-Object Tracker—0
Deep-LK for Efficient Adaptive Object Tracking—0
DeepMix: Online Auto Data Augmentation for Robust Visual Object Tracking—0
Deep Multi-Shot Network for modelling Appearance Similarity in Multi-Person Tracking applications—0
Deep Network Flow for Multi-Object Tracking—0
Deep Neural Networks—0
Deep Reinforcement Learning for Visual Object Tracking in Videos—0
Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey—0
Deep Reinforcement Learning with Iterative Shift for Visual Tracking—0
DeepScale: Online Frame Size Adaptation for Multi-object Tracking on Smart Cameras and Edge Servers—0
Deep Siamese Networks with Bayesian non-Parametrics for Video Object Tracking—0
Deformable Object Tracking with Gated Fusion—0
DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions—0
Dense Optical Flow Estimation Using Sparse Regularizers from Reduced Measurements—0
Dense Scene Multiple Object Tracking with Box-Plane Matching—0
Depth-Adaptive Computational Policies for Efficient Visual Tracking—0
Depth Estimation Analysis of Orthogonally Divergent Fisheye Cameras with Distortion Removal—0
Depth Masked Discriminative Correlation Filter—0
Depth-only Object Tracking—0
Depth Perspective-aware Multiple Object Tracking—0
Derivation of a Constant Velocity Motion Model for Visual Tracking—0
Describe and Attend to Track: Learning Natural Language guided Structural Representation and Visual Attention for Object Tracking—0
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation—0
Detection and Tracking of Multiple Mice Using Part Proposal Networks—0
Detection and Tracking of Small Objects in Sparse 3D Laser Range Data—0
Detection- and Trajectory-Level Exclusion in Multiple Object Tracking—0
DetFlowTrack: 3D Multi-object Tracking based on Simultaneous Optimization of Object Detection and Scene Flow Estimation—0
DeTrack: In-model Latent Denoising Learning for Visual Object Tracking—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
DGD: Dynamic 3D Gaussians Distillation—0
DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception—0
Dictionary Learning under Symmetries via Group Representations—0
Diff-MM: Exploring Pre-trained Text-to-Image Generation Model for Unified Multi-modal Object Tracking—0
DiffMOT: A Real-time Diffusion-based Multiple Object Tracker with Non-linear Prediction—0
DIMBA: Discretely Masked Black-Box Attack in Single Object Tracking—0
DIMM: Decoupled Multi-hierarchy Kalman Filter for 3D Object Tracking—0
DINTR: Tracking via Diffusion-based Interpolation—0
DirectTracker: 3D Multi-Object Tracking Using Direct Image Alignment and Photometric Bundle Adjustment—0
Discriminative and Efficient Label Propagation on Complementary Graphs for Multi-Object Tracking—0
Discriminative Scale Space Tracking—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