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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 161170 of 259 papers

TitleStatusHype
Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep LearningCode1
Real-time object detection method based on improved YOLOv4-tinyCode0
Fast Object Detection with Latticed Multi-Scale Feature Fusion0
R-TOD: Real-Time Object Detector with Minimized End-to-End Delay for Autonomous DrivingCode0
Deformable DETR: Deformable Transformers for End-to-End Object DetectionCode3
Artificial Intelligence Enabled Traffic Monitoring System0
Robust and Efficient Post-Processing for Video Object Detection (REPP)Code1
YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-DesignCode1
Globally-scalable Automated Target Recognition (GATR)0
Edge Network-Assisted Real-Time Object Detection Framework for Autonomous Driving0
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