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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.
Open3DTrack: Towards Open-Vocabulary 3D Multi-Object Tracking
VETRA: A Dataset for Vehicle Tracking in Aerial Imagery - New Challenges for Multi-Object Tracking
An Overview of Multi-Object Estimation via Labeled Random Finite Set
A Confidence-Aware Matching Strategy For Generalized Multi-Object Tracking
AdapTrack: Adaptive Thresholding-Based Matching For Multi-object Tracking
CAMOT: Camera Angle-aware Multi-Object Tracking
MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving
Masks and Boxes: Combining the Best of Both Worlds for Multi-Object Tracking
RockTrack: A 3D Robust Multi-Camera-Ken Multi-Object Tracking Framework
Associate Everything Detected: Facilitating Tracking-by-Detection to the Unknown
When to Extract ReID Features: A Selective Approach for Improved Multiple Object Tracking
RCBEVDet++: Toward High-accuracy Radar-Camera Fusion 3D Perception Network
LITE: A Paradigm Shift in Multi-Object Tracking with Efficient ReID Feature Integration
Gr-IoU: Ground-Intersection over Union for Robust Multi-Object Tracking with 3D Geometric Constraints
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
YOLOO: You Only Learn from Others Once
TrackSSM: A General Motion Predictor by State-Space Model
UTrack: Multi-Object Tracking with Uncertain Detections
ConsistencyTrack: A Robust Multi-Object Tracker with a Generation Strategy of Consistency Model
Decentralised Variational Inference Frameworks for Multi-object Tracking on Sensor Networks: Additional Notes
BoostTrack++: using tracklet information to detect more objects in multiple object tracking
MCTR: Multi Camera Tracking Transformer
BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Banks
MambaTrack: A Simple Baseline for Multiple Object Tracking with State Space Model
RTAT: A Robust Two-stage Association Tracker for Multi-Object Tracking
InScope: A New Real-world 3D Infrastructure-side Collaborative Perception Dataset for Open Traffic Scenarios
SharkTrack: an accurate, generalisable software for streamlining shark and ray underwater video analysis
Leveraging Foundation Models via Knowledge Distillation in Multi-Object Tracking: Distilling DINOv2 Features to FairMOT
CORT: Class-Oriented Real-time Tracking for Embedded Systems
OCTrack: Benchmarking the Open-Corpus Multi-Object Tracking
Temporal Correlation Meets Embedding: Towards a 2nd Generation of JDE-based Real-Time Multi-Object Tracking
Boosting Online 3D Multi-Object Tracking through Camera-Radar Cross Check
Lost and Found: Overcoming Detector Failures in Online Multi-Object Tracking
DroneMOT: Drone-based Multi-Object Tracking Considering Detection Difficulties and Simultaneous Moving of Drones and Objects
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.