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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 226–250 of 259 papers

TitleStatusHype
ShuffleDet: Real-Time Vehicle Detection Network in On-board Embedded UAV Imagery—0
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
Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing—0
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 Devices—0
Object detection and tracking benchmark in industry based on improved correlation filter—0
Pelee: A Real-Time Object Detection System on Mobile DevicesCode0
Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object DetectionCode0
Receptive Field Block Net for Accurate and Fast Object DetectionCode0
Dynamic Zoom-in Network for Fast Object Detection in Large Images—0
Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in VideoCode0
BlitzNet: A Real-Time Deep Network for Scene UnderstandingCode0
Yes-Net: An effective Detector Based on Global Information—0
Fast Learning and Prediction for Object Detection using Whitened CNN Features—0
DeNet: Scalable Real-time Object Detection with Directed Sparse SamplingCode0
A Fast HOG Descriptor Using Lookup Table and Integral Image—0
Wide-Residual-Inception Networks for Real-time Object Detection—0
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
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