Papers Multi-Object Tracking
“Multi-Object Tracking” 태그가 달린 논문 812편 · 필터 해제
Real-Time Automatic License Plate Recognition Using YOLOv8, SORT Tracking, and Temporal Data Interpolation
The real-time hardships of video processing seriously limit the usage of Automatic License Plate Recognition (ALPR) with application in dynamic traffic monitoring settings. High-fidelity recognition of unconstrained vari…
License Plate RecognitionMulti-Object TrackingObject DetectionA Geometry-Informed Computer Vision Method for Detecting and Examining Overtaking Vehicles From A Bicycle
Instrumented bicycle studies have produced direct field evidence on vehicle passing behavior, but extracting overtaking events from continuous rear-facing video has remained dependent on manual, frame-by-frame annotation…
Multi-Object TrackingObject DetectionCANMOT: Class-Aware Noise Modeling for Multi-Object Tracking in Autonomous Driving
Kalman filter (KF)-based multi-object tracking (MOT) remains a strong baseline for autonomous driving due to its strong performance, computational efficiency and interpretability. In most practical systems, the process n…
Computational EfficiencyMulti-Object TrackingAutonomous DrivingFace versus Body Tracking for Human-Robot Interaction: An Egocentric Dataset
Meaningful human-robot interaction (HRI) requires a robot to continuously assess user engagement through persistent user tracking. However, state-of-the-art Multi-Object Tracking models are heavily optimized for surveill…
Multi-Object TrackingAutonomous DrivingCycle Consistency in Video Object-Centric Learning
Self-supervised video Object-Centric Learning (OCL) aims to discover distinct objects and associate them across time, whereas self-supervised Multi-Object Tracking (MOT) focuses on associating pre-defined object detectio…
Multi-Object TrackingJoint 2D-3D Segmentation and Association in Street-level Imaging
Accurate interpretation of street-level imagery is essential for large-scale urban mapping and the creation of Spatial Digital Twin (SDT) environments. This work presents a unified framework for joint 2D-3D segmentation …
Multi-Object TrackingImaging Hidden Objects with Consumer LiDAR via Motion Induced Sampling
LiDARs are being increasingly deployed for consumer imaging in handheld, wearable, and robotic applications. These sensors can capture the time-of-flight of light at picosecond resolution, which in principle, enables the…
Multi-Object TrackingCamera Localization3D ReconstructionMulti-Object Tracking Consistently Improves Wildlife Inference
Camera traps have become a common tool for wildlife monitoring efforts in ecological research and biodiversity conservation. Wildlife classification models have benefited from the increase in wildlife visual data. These …
Multi-Object TrackingCOAL: Counterfactual and Observation-Enhanced Alignment Learning for Discriminative Referring Multi-Object Tracking
Referring Multi-Object Tracking (RMOT) faces a fundamental structural contradiction between the high-discriminability demand and the sparse semantic supervision. This mismatch is particularly acute in highly homogeneous …
Multi-Object TrackingClip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking
Multi-Object Tracking (MOT) in dynamic environments relies on robust temporal reasoning to maintain consistent object identities over time. Transformer-based end-to-end MOT models achieve strong performance by explicitly…
Multi-Object TrackingActive LearningAutomated high-frequency quantification of fish communities and biomass using computer vision
Quantifying fish community structure is essential for understanding biodiversity and ecosystem responses in a changing environment, yet existing survey methods provide limited high-frequency, quantitative observations. C…
Multi-Object Tracking3D ReconstructionCalibFree: Self-Supervised View Feature Separation for Calibration-Free Multi-Camera Multi-Object Tracking
Multi-camera multi-object tracking (MCMOT) faces significant challenges in maintaining consistent object identities across varying camera perspectives, particularly when precise calibration and extensive annotations are …
Representation LearningMulti-Object TrackingSAMOFT: Robust Multi-Object Tracking via Region and Flow
Multi-object tracking (MOT) is a fundamental task in computer vision that requires continuously tracking multiple targets while maintaining consistent identities across frames. However, most existing approaches primarily…
Multi-Object TrackingViewSAM: Learning View-aware Cross-modal Semantics for Weakly Supervised Cross-view Referring Multi-Object Tracking
Cross-view Referring Multi-Object Tracking (CRMOT) aims to track multiple objects specified by natural language across multiple camera views, with globally consistent identities. Despite recent progress, existing methods…
Multi-Object TrackingTime-series Meets Complex Motion Modeling: Robust and Computational-effective Motion Predictor for Multi-object Tracking
Multi-object tracking (MOT) is critical in numerous real-world applications, including surveillance, autonomous driving, and robotics. Accurately predicting object motion is fundamental to MOT, but current methods strugg…
Multi-Object TrackingAutonomous DrivingMotion-Driven Multi-Object Tracking of Model Organisms in Space Science Experiments
Automated animal behavior analysis relies on long-term, interpretable individual trajectories; however, multi-animal tracking in space science experimental videos remains highly challenging due to weak appearance cues, l…
Multi-Object TrackingNo Pedestrian Left Behind: Real-Time Detection and Tracking of Vulnerable Road Users for Adaptive Traffic Signal Control
Current pedestrian crossing signals operate on fixed timing without adjustment to pedestrian behavior, which can leave vulnerable road users (VRUs) such as the elderly, disabled, or distracted pedestrians stranded when t…
Multi-Object TrackingObject DetectionSAMIDARE: Advanced Tracking-by-Segmentation for Dense Scenarios
Automated sports analysis demands robust multi-object tracking (MOT), yet segmentation-based methods often struggle with mask errors and ID switches in dense scenes. We propose SAMIDARE, a framework that enhances SAM2MOT…
Multi-Object TrackingRailway Artificial Intelligence Learning Benchmark (RAIL-BENCH): A Benchmark Suite for Perception in the Railway Domain
Automated train operation on existing railway infrastructure requires robust camera-based perception, yet the railway domain lacks public benchmark suites with standardized evaluation protocols that would enable reproduc…
Multi-Object TrackingObject DetectionVisual OdometryLine DetectionUfil: A Unified Framework for Infrastructure-based Localization
Infrastructure-based localization enhances road safety and traffic management by providing state estimates of road users. Development is hindered by fragmented, application-specific stacks that tightly couple perception,…
Multi-Object Tracking