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 1051–1100 of 1966 papers

TitleStatusHype
Compensation Tracker: Reprocessing Lost Object for Multi-Object Tracking—0
Computer Vision-based Accident Detection in Traffic Surveillance—0
Computer Vision based Animal Collision Avoidance Framework for Autonomous Vehicles—0
Computer Vision Toolkit for Non-invasive Monitoring of Factory Floor Artifacts—0
Concurrent Tracking of Inliers and Outliers—0
Confidence-Based Dynamic Classifier Combination For Mean-Shift Tracking—0
Confidence Trigger Detection: Accelerating Real-time Tracking-by-detection Systems—0
Conjugation Invariant Learning with Neural Networks—0
Consensus-based Distributed Variational Multi-object Tracker in Multi-Sensor Network—0
Context-aware Visual Tracking with Joint Meta-updating—0
Continuity-Discrimination Convolutional Neural Network for Visual Object Tracking—0
Rethinking Temporal Object Detection from Robotic Perspectives—0
Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency—0
ControlLoc: Physical-World Hijacking Attack on Visual Perception in Autonomous Driving—0
Convolutional Neural Networks for Non-iterative Reconstruction of Compressively Sensed Images—0
Convolutional Neural Networks in Orthodontics: a review—0
Convolutional Recurrent Predictor: Implicit Representation for Multi-target Filtering and Tracking—0
Convolutional Regression for Visual Tracking—0
Convolutional Unscented Kalman Filter for Multi-Object Tracking with Outliers—0
Coreset-Based Adaptive Tracking—0
CorrBEV: Multi-View 3D Object Detection by Correlation Learning with Multi-modal Prototypes—0
Correlation Filters with Limited Boundaries—0
Correlation filter tracking with adaptive proposal selection for accurate scale estimation—0
Correlation Pyramid Network for 3D Single Object Tracking—0
CORT: Class-Oriented Real-time Tracking for Embedded Systems—0
COST: Contrastive One-Stage Transformer for Vision-Language Small Object Tracking—0
Joint Counting, Detection and Re-Identification for Multi-Object Tracking—0
Just Functioning as a Hook for Two-Stage Referring Multi-Object Tracking—0
Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics—0
Cross-Modal Object Tracking: Modality-Aware Representations and A Unified Benchmark—0
Cross-Modal Pattern-Propagation for RGB-T Tracking—0
CrossTracker: Robust Multi-modal 3D Multi-Object Tracking via Cross Correction—0
CSAOT: Cooperative Multi-Agent System for Active Object Tracking—0
CVPR19 Tracking and Detection Challenge: How crowded can it get?—0
CXTrack: Improving 3D Point Cloud Tracking with Contextual Information—0
CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions—0
D3S - A Discriminative Single Shot Segmentation Tracker—0
DART: Distribution Aware Retinal Transform for Event-based Cameras—0
DashNet: A Hybrid Artificial and Spiking Neural Network for High-speed Object Tracking—0
Data-Driven Object Tracking: Integrating Modular Neural Networks into a Kalman Framework—0
Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices—0
DAWN: Dual Augmented Memory Network for Unsupervised Video Object Tracking—0
Decentralised Variational Inference Frameworks for Multi-object Tracking on Sensor Networks: Additional Notes—0
DeconfuseTrack:Dealing with Confusion for Multi-Object Tracking—0
DeconfuseTrack: Dealing with Confusion for Multi-Object Tracking—0
Deconvolutional Networks for Point-Cloud Vehicle Detection and Tracking in Driving Scenarios—0
DEEGITS: Deep Learning based Framework for Measuring Heterogenous Traffic State in Challenging Traffic Scenarios—0
Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping—0
Deep Active Contours for Real-time 6-DoF Object Tracking—0
Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object 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