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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 171180 of 259 papers

TitleStatusHype
Object Detection in the Context of Mobile Augmented Reality0
Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged NetworksCode1
Traffic Prediction Framework for OpenStreetMap using Deep Learning based Complex Event Processing and Open Traffic CamerasCode0
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
YOLOv4: Optimal Speed and Accuracy of Object DetectionCode3
TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection SystemsCode1
Real Time Multi-Class Object Detection and Recognition Using Vision Augmentation Algorithm0
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