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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 8190 of 259 papers

TitleStatusHype
Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and MethodCode1
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object DetectionCode0
Replication Study and Benchmarking of Real-Time Object Detection ModelsCode0
Relief R-CNN : Utilizing Convolutional Features for Fast Object DetectionCode0
R-FCN: Object Detection via Region-based Fully Convolutional NetworksCode0
DeNet: Scalable Real-time Object Detection with Directed Sparse SamplingCode0
Attentional PointNet for 3D-Object Detection in Point CloudsCode0
Receptive Field Block Net for Accurate and Fast Object DetectionCode0
Real-time object detection method based on improved YOLOv4-tinyCode0
Real-Time Object Detection on High-Voltage Powerlines Using an Unmanned Aerial Vehicle (UAV)Code0
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