SOTAVerified

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 191200 of 259 papers

TitleStatusHype
CenterMask : Real-Time Anchor-Free Instance SegmentationCode1
A Proposed Artificial intelligence Model for Real-Time Human Action Localization and Tracking0
xYOLO: A Model For Real-Time Object Detection In Humanoid Soccer On Low-End Hardware0
A Deep Learning Framework for Detection of Targets in Thermal Images to Improve Firefighting0
Role of Spatial Context in Adversarial Robustness for Object DetectionCode0
F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds0
HarDNet: A Low Memory Traffic NetworkCode1
Training-Time-Friendly Network for Real-Time Object DetectionCode0
SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV ApplicationsCode0
Attentional PointNet for 3D-Object Detection in Point CloudsCode0
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