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Multiple Object Tracking
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Multiple Object Tracking is the problem of automatically identifying multiple objects in a video and representing them as a set of trajectories with high accuracy.
Source: SOT for MOT
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
LiDAR MOT-DETR: A LiDAR-based Two-Stage Transformer for 3D Multiple Object Tracking
Using Cross-Domain Detection Loss to Infer Multi-Scale Information for Improved Tiny Head Tracking
Towards Accurate State Estimation: Kalman Filter Incorporating Motion Dynamics for 3D Multi-Object Tracking
History-Aware Transformation of ReID Features for Multiple Object Tracking
OVTR: End-to-End Open-Vocabulary Multiple Object Tracking with Transformer
INTACT: Inducing Noise Tolerance through Adversarial Curriculum Training for LiDAR-based Safety-Critical Perception and Autonomy
Heterogeneous Graph Transformer for Multiple Tiny Object Tracking in RGB-T Videos
A2VIS: Amodal-Aware Approach to Video Instance Segmentation
Enhancing Thermal MOT: A Novel Box Association Method Leveraging Thermal Identity and Motion Similarity
GTA: Global Tracklet Association for Multi-Object Tracking in Sports
SIRA: Scalable Inter-frame Relation and Association for Radar Perception
Is Multiple Object Tracking a Matter of Specialization?
TrackMe:A Simple and Effective Multiple Object Tracking Annotation Tool
Enhancing In-vehicle Multiple Object Tracking Systems with Embeddable Ising Machines
Enhanced Kalman with Adaptive Appearance Motion SORT for Grounded Generic Multiple Object Tracking
Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking
Samba: Synchronized Set-of-Sequences Modeling for Multiple Object Tracking
Automated Surgical Skill Assessment in Endoscopic Pituitary Surgery using Real-time Instrument Tracking on a High-fidelity Bench-top Phantom
Walker: Self-supervised Multiple Object Tracking by Walking on Temporal Appearance Graphs
MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving
STCMOT: Spatio-Temporal Cohesion Learning for UAV-Based Multiple Object Tracking
SLAck: Semantic, Location, and Appearance Aware Open-Vocabulary Tracking
Associate Everything Detected: Facilitating Tracking-by-Detection to the Unknown
FACT: Feature Adaptive Continual-learning Tracker for Multiple Object Tracking
When to Extract ReID Features: A Selective Approach for Improved Multiple Object Tracking
TP-GMOT: Tracking Generic Multiple Object by Textual Prompt with Motion-Appearance Cost (MAC) SORT
One Homography is All You Need: IMM-based Joint Homography and Multiple Object State Estimation
TrackSSM: A General Motion Predictor by State-Space Model
Estimating Dynamic Flow Features in Groups of Tracked Objects
BoostTrack++: using tracklet information to detect more objects in multiple object tracking
CHOTA: A Higher Order Accuracy Metric for Cell Tracking
MambaTrack: A Simple Baseline for Multiple Object Tracking with State Space Model
GSLAMOT: A Tracklet and Query Graph-based Simultaneous Locating, Mapping, and Multiple Object Tracking System
Visible-Thermal Multiple Object Tracking: Large-scale Video Dataset and Progressive Fusion Approach
Leveraging Foundation Models via Knowledge Distillation in Multi-Object Tracking: Distilling DINOv2 Features to FairMOT
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