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Real-Time Object Detection

Real-Time Object Detection is a computer vision task that involves identifying and locating objects of interest in real-time video sequences with fast inference while maintaining a base level of accuracy.

This is typically solved using algorithms that combine object detection and tracking techniques to accurately detect and track objects in real-time. They use a combination of feature extraction, object proposal generation, and classification to detect and localize objects of interest.

( Image credit: CenterNet )

Papers

Showing 5160 of 259 papers

TitleStatusHype
YotoR-You Only Transform One Representation0
YOLOv10: Real-Time End-to-End Object DetectionCode11
Replication Study and Benchmarking of Real-Time Object Detection ModelsCode0
Performance Evaluation of Real-Time Object Detection for Electric ScootersCode0
PointCompress3D: A Point Cloud Compression Framework for Roadside LiDARs in Intelligent Transportation Systems0
GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning0
YOLOv5-6D: Advancing 6-DoF Instrument Pose Estimation in Variable X-Ray Imaging GeometriesCode2
Prototipo de un Contador Bidireccional Automático de Personas basado en sensores de visión 3D0
Inception-YOLO: Computational cost and accuracy improvement of the YOLOv5 model based on employing modified CSP, SPPF, and inception modules0
Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion HeadCode5
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