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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 181190 of 259 papers

TitleStatusHype
R-TOSS: A Framework for Real-Time Object Detection using Semi-Structured Pruning0
Screening COVID-19 cases using Deep Neural Networks with X-ray images0
Self-Configurable Stabilized Real-Time Detection Learning for Autonomous Driving Applications0
ShuffleDet: Real-Time Vehicle Detection Network in On-board Embedded UAV Imagery0
SlowPerception: Physical-World Latency Attack against Visual Perception in Autonomous Driving0
Small Object Detection by DETR via Information Augmentation and Adaptive Feature Fusion0
Small Object Detection using Deep Learning0
Speed Up Object Detection on Gigapixel-Level Images With Patch Arrangement0
SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Driving0
Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection0
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