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Multi-Object Tracking
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Multi-Object Tracking is a task in computer vision that involves detecting and tracking multiple objects within a video sequence. The goal is to identify and locate objects of interest in each frame and then associate them across frames to keep track of their movements over time. This task is challenging due to factors such as occlusion, motion blur, and changes in object appearance, and is typically solved using algorithms that integrate object detection and data association techniques.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
YOLOv8-SMOT: An Efficient and Robust Framework for Real-Time Small Object Tracking via Slice-Assisted Training and Adaptive Association
Probabilistic Trajectory GOSPA: A Metric for Uncertainty-Aware Multi-Object Tracking Performance Evaluation
Deep Learning-Based Multi-Object Tracking: A Comprehensive Survey from Foundations to State-of-the-Art
ReaMOT: A Benchmark and Framework for Reasoning-based Multi-Object Tracking
FusionTrack: End-to-End Multi-Object Tracking in Arbitrary Multi-View Environment
Distributed Expectation Propagation for Multi-Object Tracking over Sensor Networks
LiDAR MOT-DETR: A LiDAR-based Two-Stage Transformer for 3D Multiple Object Tracking
Towards Accurate State Estimation: Kalman Filter Incorporating Motion Dynamics for 3D Multi-Object Tracking
Asynchronous Multi-Object Tracking with an Event Camera
Optimizing Indoor Farm Monitoring Efficiency Using UAV: Yield Estimation in a GNSS-Denied Cherry Tomato Greenhouse
CAMELTrack: Context-Aware Multi-cue ExpLoitation for Online Multi-Object Tracking
The Mean of Multi-Object Trajectories
Intelligent driving vehicle front multi-target tracking and detection based on YOLOv5 and point cloud 3D projection
Multi-Object Tracking for Collision Avoidance Using Multiple Cameras in Open RAN Networks
SAM2MOT: A Novel Paradigm of Multi-Object Tracking by Segmentation
TQD-Track: Temporal Query Denoising for 3D Multi-Object Tracking
Data-Driven Object Tracking: Integrating Modular Neural Networks into a Kalman Framework
Attention-Aware Multi-View Pedestrian Tracking
Deep LG-Track: An Enhanced Localization-Confidence-Guided Multi-Object Tracker
TrackID3x3: A Dataset and Algorithm for Multi-Player Tracking with Identification and Pose Estimation in 3x3 Basketball Full-court Videos
OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering
History-Aware Transformation of ReID Features for Multiple Object Tracking
Cognitive Disentanglement for Referring Multi-Object Tracking
How good are deep learning methods for automated road safety analysis using video data? An experimental study
Just Functioning as a Hook for Two-Stage Referring Multi-Object Tracking
Omnidirectional Multi-Object Tracking
Easy-Poly: A Easy Polyhedral Framework For 3D Multi-Object Tracking
CRTrack: Low-Light Semi-Supervised Multi-object Tracking Based on Consistency Regularization
MEX: Memory-efficient Approach to Referring Multi-Object Tracking
IMM-MOT: A Novel 3D Multi-object Tracking Framework with Interacting Multiple Model Filter
OneTrack-M: A multitask approach to transformer-based MOT models
YOLO11-JDE: Fast and Accurate Multi-Object Tracking with Self-Supervised Re-ID
PD-SORT: Occlusion-Robust Multi-Object Tracking Using Pseudo-Depth Cues
Spatio-temporal Graph Learning on Adaptive Mined Key Frames for High-performance Multi-Object Tracking
TimberVision: A Multi-Task Dataset and Framework for Log-Component Segmentation and Tracking in Autonomous Forestry Operations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.