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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 121130 of 259 papers

TitleStatusHype
SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Driving0
Slim-neck by GSConv: A lightweight-design for real-time detector architecturesCode2
Vision Transformer Adapter for Dense PredictionsCode3
PP-YOLOE: An evolved version of YOLOCode4
Real-time Object Detection for Streaming PerceptionCode2
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object DetectionCode4
BED: A Real-Time Object Detection System for Edge DevicesCode1
A ConvNet for the 2020sCode5
Small Object Detection using Deep Learning0
Speed Up Object Detection on Gigapixel-Level Images With Patch Arrangement0
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