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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 211220 of 259 papers

TitleStatusHype
Pelee: A Real-Time Object Detection System on Mobile DevicesCode0
Pelee: A Real-Time Object Detection System on Mobile DevicesCode0
Parallel Detection for Efficient Video Analytics at the EdgeCode0
Network transferability of adversarial patches in real-time object detectionCode0
Traffic Prediction Framework for OpenStreetMap using Deep Learning based Complex Event Processing and Open Traffic CamerasCode0
Training-Time-Friendly Network for Real-Time Object DetectionCode0
Attentional PointNet for 3D-Object Detection in Point CloudsCode0
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object DetectionCode0
Training Multi-Object Detector by Estimating Bounding Box Distribution for Input ImageCode0
Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm SurveillanceCode0
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