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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 251259 of 259 papers

TitleStatusHype
YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU ComputersCode0
DroNet: Efficient convolutional neural network detector for real-time UAV applicationsCode0
Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm SurveillanceCode0
YOLOSA: Object detection based on 2D local feature superimposed self-attentionCode0
DiCENet: Dimension-wise Convolutions for Efficient NetworksCode0
SpineNet: Learning Scale-Permuted Backbone for Recognition and LocalizationCode0
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous DrivingCode0
Attentional PointNet for 3D-Object Detection in Point CloudsCode0
Pelee: A Real-Time Object Detection System on Mobile DevicesCode0
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