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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
FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds0
Looking Fast and Slow: Memory-Guided Mobile Video Object DetectionCode0
Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection0
Pelee: A Real-Time Object Detection System on Mobile DevicesCode0
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural NetworkCode0
Fast Object Detection in Compressed Video0
A Convolutional Neural Network based Live Object Recognition System as Blind Aid0
ShuffleDet: Real-Time Vehicle Detection Network in On-board Embedded UAV Imagery0
YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU ComputersCode0
Fast and accurate object detection in high resolution 4K and 8K video using GPUsCode0
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