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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

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Showing 241–250 of 259 papers

TitleStatusHype
Wide-Residual-Inception Networks for Real-time Object Detection—0
YOLO9000: Better, Faster, StrongerCode1
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous DrivingCode0
PVANet: Lightweight Deep Neural Networks for Real-time Object DetectionCode0
Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene—0
Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks—0
PVANET: Deep but Lightweight Neural Networks for Real-time Object DetectionCode0
A 58.6mW Real-Time Programmable Object Detector with Multi-Scale Multi-Object Support Using Deformable Parts Model on 1920x1080 Video at 30fps—0
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object DetectionCode0
R-FCN: Object Detection via Region-based Fully Convolutional NetworksCode0
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