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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.
Cross-View Referring Multi-Object Tracking
Tell Me What to Track: Infusing Robust Language Guidance for Enhanced Referring Multi-Object Tracking
Heterogeneous Graph Transformer for Multiple Tiny Object Tracking in RGB-T Videos
Post-Hoc MOTS: Exploring the Capabilities of Time-Symmetric Multi-Object Tracking
TGOSPA Metric Parameters Selection and Evaluation for Visual Multi-object Tracking
Enhanced Multi-Object Tracking Using Pose-based Virtual Markers in 3x3 Basketball
Beyond Boxes: Mask-Guided Spatio-Temporal Feature Aggregation for Video Object Detection
Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks
MCBLT: Multi-Camera Multi-Object 3D Tracking in Long Videos
SpaRC: Sparse Radar-Camera Fusion for 3D Object Detection
CrossTracker: Robust Multi-modal 3D Multi-Object Tracking via Cross Correction
Visual SLAMMOT Considering Multiple Motion Models
FastTrackTr:Towards Fast Multi-Object Tracking with Transformers
Learning a Neural Association Network for Self-supervised Multi-Object Tracking
MOT FCG++: Enhanced Representation of Spatio-temporal Motion and Appearance Features
DEEGITS: Deep Learning based Framework for Measuring Heterogenous Traffic State in Challenging Traffic Scenarios
3D Multi-Object Tracking with Semi-Supervised GRU-Kalman Filter
GTA: Global Tracklet Association for Multi-Object Tracking in Sports
Track Any Peppers: Weakly Supervised Sweet Pepper Tracking Using VLMs
SynCL: A Synergistic Training Strategy with Instance-Aware Contrastive Learning for End-to-End Multi-Camera 3D Tracking
BuckTales : A multi-UAV dataset for multi-object tracking and re-identification of wild antelopes
PKF: Probabilistic Data Association Kalman Filter for Multi-Object Tracking
Multi-object Tracking by Detection and Query: an efficient end-to-end manner
Graph-Based Multi-Modal Sensor Fusion for Autonomous Driving
HopTrack: A Real-time Multi-Object Tracking System for Embedded Devices
IP-MOT: Instance Prompt Learning for Cross-Domain Multi-Object Tracking
SFTrack: A Robust Scale and Motion Adaptive Algorithm for Tracking Small and Fast Moving Objects
OVT-B: A New Large-Scale Benchmark for Open-Vocabulary Multi-Object Tracking
MPT: A Large-scale Multi-Phytoplankton Tracking Benchmark
3D Multi-Object Tracking Employing MS-GLMB Filter for Autonomous Driving
Temporal-Enhanced Multimodal Transformer for Referring Multi-Object Tracking and Segmentation
Motion-guided small MAV detection in complex and non-planar scenes
VOVTrack: Exploring the Potentiality in Videos for Open-Vocabulary Object Tracking
Efficient Multi-Object Tracking on Edge Devices via Reconstruction-Based Channel Pruning
Enhanced Kalman with Adaptive Appearance Motion SORT for Grounded Generic Multiple Object Tracking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.