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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

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Showing 141–150 of 259 papers

TitleStatusHype
FasterX: Real-Time Object Detection Based on Edge GPUs for UAV Applications—0
Fast Learning and Prediction for Object Detection using Whitened CNN Features—0
Fast Object Detection in Compressed Video—0
Fast Object Detection with a Machine Learning Edge Device—0
Fast Object Detection with Entropy-Driven Evaluation—0
Fast Object Detection with Latticed Multi-Scale Feature Fusion—0
F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds—0
First qualitative observations on deep learning vision model YOLO and DETR for automated driving in Austria—0
FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds—0
Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene—0
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