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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 6170 of 259 papers

TitleStatusHype
Scaled-YOLOv4: Scaling Cross Stage Partial NetworkCode1
Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep LearningCode1
Robust and Efficient Post-Processing for Video Object Detection (REPP)Code1
YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-DesignCode1
Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged NetworksCode1
Understanding Object Detection Through An Adversarial LensCode1
End-to-End Object Detection with TransformersCode1
Towards Streaming PerceptionCode1
Automated detection of COVID-19 cases using deep neural networks with X-ray imagesCode1
TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection SystemsCode1
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