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 1651–1700 of 1966 papers

TitleStatusHype
Collaborative Deep Reinforcement Learning for Multi-Object Tracking—0
Multi-object Tracking with Neural Gating Using Bilinear LSTM—0
Triplet Loss in Siamese Network for Object Tracking—0
Cross-Modal Ranking with Soft Consistency and Noisy Labels for Robust RGB-T TrackingCode0
Deep Reinforcement Learning with Iterative Shift for Visual Tracking—0
Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data—0
Deconvolutional Networks for Point-Cloud Vehicle Detection and Tracking in Driving Scenarios—0
Multi-Branch Siamese Networks with Online Selection for Object Tracking—0
Estimating Metric Poses of Dynamic Objects Using Monocular Visual-Inertial Fusion—0
Distractor-aware Siamese Networks for Visual Object TrackingCode0
Dual approach for object tracking based on optical flow and swarm intelligence—0
End-to-end Active Object Tracking and Its Real-world Deployment via Reinforcement Learning—0
Tracklet Association Tracker: An End-to-End Learning-based Association Approach for Multi-Object Tracking—0
Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual TrackingCode0
Learning The Sequential Temporal Information with Recurrent Neural Networks—0
Variance Reduction for Reinforcement Learning in Input-Driven Environments—0
Combining Background Subtraction Algorithms with Convolutional Neural Network—0
Subpixel-Precise Tracking of Rigid Objects in Real-time—0
A Region-based Gauss-Newton Approach to Real-Time Monocular Multiple Object TrackingCode0
Efficient and Consistent Adversarial Bipartite Matching—0
Antithetic and Monte Carlo kernel estimators for partial rankings—0
Deep Similarity Metric Learning for Real-Time Pedestrian Tracking—0
Learning to Update for Object Tracking with Recurrent Meta-learner—0
A Connectome Based Hexagonal Lattice Convolutional Network Model of the Drosophila Visual System—0
Robust Object Tracking with Crow Search Optimized Multi-cue Particle Filter—0
Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object Tracking—0
Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual TrackingCode0
WILDTRACK: A Multi-Camera HD Dataset for Dense Unscripted Pedestrian Detection—0
High Performance Visual Tracking With Siamese Region Proposal NetworkCode0
Hyperparameter Optimization for Tracking With Continuous Deep Q-Learning—0
Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering—0
MobiFace: A Novel Dataset for Mobile Face Tracking in the WildCode0
Particle Filter Networks with Application to Visual LocalizationCode0
RGB-T Object Tracking:Benchmark and Baseline—0
Measurement-wise Occlusion in Multi-object Tracking—0
A Continuous, Full-scope, Spatio-temporal Tracking Metric based on KL-divergence—0
Object Tracking with Correlation Filters using Selective Single Background Patch—0
Visual Object Tracking: The Initialisation ProblemCode0
MV-YOLO: Motion Vector-aided Tracking by Semantic Object Detection—0
Object Tracking in Satellite Videos Based on a Multi-Frame Optical Flow Tracker—0
Vision Meets Drones: A Challenge—0
Large Margin Structured Convolution Operator for Thermal Infrared Object Tracking—0
Unveiling the Power of Deep Tracking—0
State-Space Abstractions for Probabilistic Inference: A Systematic Review—0
A Novel Low-cost FPGA-based Real-time Object Tracking System—0
Head Mounted Pupil Tracking Using Convolutional Neural Network—0
Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking—0
FPAN: Fine-grained and Progressive Attention Localization Network for Data Retrieval—0
Learning a Robust Society of Tracking Parts using Co-occurrence Constraints—0
Evaluation of Object Trackers in Distorted Surveillance Videos—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