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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 131140 of 259 papers

TitleStatusHype
Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm SurveillanceCode0
Real-time Traffic Object Detection for Autonomous Driving0
Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approach0
Small Object Detection by DETR via Information Augmentation and Adaptive Feature Fusion0
Real-Time Object Detection in Occluded Environment with Background Cluttering Effects Using Deep Learning0
BiSwift: Bandwidth Orchestrator for Multi-Stream Video Analytics on Edge0
First qualitative observations on deep learning vision model YOLO and DETR for automated driving in Austria0
Liquid Leak Detection Using Thermal Images0
Object-centric Cross-modal Feature Distillation for Event-based Object Detection0
DEYOv3: DETR with YOLO for Real-time Object DetectionCode0
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