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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 221230 of 259 papers

TitleStatusHype
Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing0
MnasNet: Platform-Aware Neural Architecture Search for MobileCode1
Joint Anchor-Feature Refinement for Real-Time Accurate Object Detection in Images and VideosCode1
Visual Mesh: Real-time Object Detection Using Constant Sample DensityCode0
DroNet: Efficient convolutional neural network detector for real-time UAV applicationsCode0
Inference of Quantized Neural Networks on Heterogeneous All-Programmable Devices0
Object detection and tracking benchmark in industry based on improved correlation filter0
Pelee: A Real-Time Object Detection System on Mobile DevicesCode0
YOLOv3: An Incremental ImprovementCode1
Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object DetectionCode0
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