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 501–550 of 1966 papers

TitleStatusHype
Revisiting the details when evaluating a visual trackerCode0
ReynoldsFlow: Exquisite Flow Estimation via Reynolds Transport TheoremCode0
Rethinking Vision Transformer for Object Centric Foundation ModelsCode0
Reproducibility Study on Adversarial Attacks Against Robust Transformer TrackersCode0
Risk-Aware Robot Control in Dynamic Environments Using Belief Control Barrier FunctionsCode0
BOBBY2: Buffer Based Robust High-Speed Object TrackingCode0
β-Multivariational Autoencoder for Entangled Representation Learning in Video FramesCode0
Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-IdentificationCode0
Real-Time Event-Based Tracking and Detection for Maritime EnvironmentsCode0
Real-Time Multiple Object Tracking - A Study on the Importance of SpeedCode0
Reliable Object Tracking by Multimodal Hybrid Feature Extraction and Transformer-Based FusionCode0
Distractor-aware Siamese Networks for Visual Object TrackingCode0
Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic ObjectsCode0
DistNet: Deep Tracking by displacement regression: application to bacteria growing in the Mother MachineCode0
(Quasi)Periodicity Quantification in Video Data, Using TopologyCode0
Representation Alignment Contrastive Regularization for Multi-Object TrackingCode0
RobMOT: Robust 3D Multi-Object Tracking by Observational Noise and State Estimation Drift Mitigation on LiDAR PointCloudCode0
Discriminative and Robust Online Learning for Siamese Visual TrackingCode0
Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object SegmentationCode0
A Multi-body Tracking Framework -- From Rigid Objects to Kinematic StructuresCode0
Predictor-corrector algorithms for stochastic optimization under gradual distribution shiftCode0
Psychlab: A Psychology Laboratory for Deep Reinforcement Learning AgentsCode0
DiffusionTrack: Point Set Diffusion Model for Visual Object TrackingCode0
A Modular Pipeline for 3D Object Tracking Using RGB CamerasCode0
DHER: Hindsight Experience Replay for Dynamic GoalsCode0
Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object TrackingCode0
Beyond Kalman Filters: Deep Learning-Based Filters for Improved Object TrackingCode0
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual TrackingCode0
Detection-aware multi-object tracking evaluationCode0
Particle Filter Networks with Application to Visual LocalizationCode0
Detect-and-Track: Efficient Pose Estimation in VideosCode0
A Low-Computational Video Synopsis Framework with a Standard DatasetCode0
Benchmarking YOLOv5 and YOLOv7 models with DeepSORT for droplet tracking applicationsCode0
Online multi-object tracking via robust collaborative model and sample selectionCode0
Online Multi-Object Tracking with Dual Matching Attention NetworksCode0
A comparison of RL-based and PID controllers for 6-DOF swimming robots: hybrid underwater object trackingCode0
Depth-Based Object Tracking Using a Robust Gaussian FilterCode0
AlignNet-3D: Fast Point Cloud Registration of Partially Observed ObjectsCode0
Online Multi-camera People Tracking with Spatial-temporal Mechanism and Anchor-feature Hierarchical ClusteringCode0
Online Object Tracking, Learning and Parsing with And-Or GraphsCode0
DensSiam: End-to-End Densely-Siamese Network with Self-Attention Model for Object TrackingCode0
3D Multi-Object Tracking Employing MS-GLMB Filter for Autonomous DrivingCode0
Observation Centric and Central Distance Recovery on Sports Player TrackingCode0
On Designing Light-Weight Object Trackers through Network Pruning: Use CNNs or Transformers?Code0
Object Detection from Video Tubelets with Convolutional Neural NetworksCode0
Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural NetworksCode0
No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARsCode0
Object Detection in Videos with Tubelet Proposal NetworksCode0
Neural Enhanced Belief Propagation for Multiobject TrackingCode0
Need for Speed: A Benchmark for Higher Frame Rate Object TrackingCode0
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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