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 651–700 of 1966 papers

TitleStatusHype
A Hypergradient Approach to Robust Regression without Correspondence—0
A Certifiable Security Patch for Object Tracking in Self-Driving Systems via Historical Deviation Modeling—0
Deep Learning-Based Multi-Object Tracking: A Comprehensive Survey from Foundations to State-of-the-Art—0
A Hybrid Data Association Framework for Robust Online Multi-Object Tracking—0
Deep learning based 2.5D flow field estimation for maximum intensity projections of 4D optical coherence tomography—0
Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey—0
AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility—0
RBCN: Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs—0
Deep HM-SORT: Enhancing Multi-Object Tracking in Sports with Deep Features, Harmonic Mean, and Expansion IOU—0
AutoSelect: Automatic and Dynamic Detection Selection for 3D Multi-Object Tracking—0
Accurate Object Association and Pose Updating for Semantic SLAM—0
Deep Similarity Metric Learning for Real-Time Pedestrian Tracking—0
Improving tracking with a tracklet associator—0
Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object Tracking—0
Autoregressive Sequential Pretraining for Visual Tracking—0
A Hybrid Approach To Real-Time Multi-Object Tracking—0
Deep Active Contours for Real-time 6-DoF Object Tracking—0
Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping—0
Automatic tracking of protein vesicles—0
Accurate Anchor Free Tracking—0
DEEGITS: Deep Learning based Framework for Measuring Heterogenous Traffic State in Challenging Traffic Scenarios—0
Deconvolutional Networks for Point-Cloud Vehicle Detection and Tracking in Driving Scenarios—0
Automatic Tracker Selection w.r.t Object Detection Performance—0
DeconfuseTrack: Dealing with Confusion for Multi-Object Tracking—0
DeconfuseTrack:Dealing with Confusion for Multi-Object Tracking—0
Automatic Parameter Adaptation for Multi-object Tracking—0
A Hierarchical Graph Signal Processing Approach to Inference from Spatiotemporal Signals—0
Improving Multiple Object Tracking With Single Object Tracking—0
Decentralised Variational Inference Frameworks for Multi-object Tracking on Sensor Networks: Additional Notes—0
Automatic individual pig detection and tracking in surveillance videos—0
1st Place Solutions for Waymo Open Dataset Challenges -- 2D and 3D Tracking—0
DAWN: Dual Augmented Memory Network for Unsupervised Video Object Tracking—0
Automatic Controlling Fish Feeding Machine using Feature Extraction of Nutriment and Ripple Behavior—0
Improving Model Drift for Robust Object Tracking—0
Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices—0
Data-Driven Object Tracking: Integrating Modular Neural Networks into a Kalman Framework—0
Automated Surgical Skill Assessment in Endoscopic Pituitary Surgery using Real-time Instrument Tracking on a High-fidelity Bench-top Phantom—0
DashNet: A Hybrid Artificial and Spiking Neural Network for High-speed Object Tracking—0
A Graphical Social Topology Model for Multi-Object Tracking—0
ABCTracker: an easy-to-use, cloud-based application for tracking multiple objects—0
Improving Multiple Object Tracking with Optical Flow and Edge Preprocessing—0
Improving trajectory continuity in drone-based crowd monitoring using a set of minimal-cost techniques and deep discriminative correlation filters—0
Inference for multiple object tracking: A Bayesian nonparametric approach—0
Intelligent Intersection: Two-Stream Convolutional Networks for Real-time Near Accident Detection in Traffic Video—0
DART: Distribution Aware Retinal Transform for Event-based Cameras—0
Nerve Block Target Localization and Needle Guidance for Autonomous Robotic Ultrasound Guided Regional Anesthesia—0
Automated learning with a probabilistic programming language: Birch—0
D3S - A Discriminative Single Shot Segmentation Tracker—0
Aggregation Signature for Small Object Tracking—0
Image Pixel Fusion for Human Face Recognition—0
Show:102550
← PrevPage 14 of 40Next →
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