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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 111120 of 259 papers

TitleStatusHype
Object Detection in Specific Traffic Scenes using YOLOv20
Leveraging the Edge and Cloud for V2X-Based Real-Time Object Detection in Autonomous Driving0
Globally-scalable Automated Target Recognition (GATR)0
Lightweight Object Detection: A Study Based on YOLOv7 Integrated with ShuffleNetv2 and Vision Transformer0
GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning0
Liquid Leak Detection Using Thermal Images0
Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene0
MambaNeXt-YOLO: A Hybrid State Space Model for Real-time Object Detection0
FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds0
CorrDiff: Adaptive Delay-aware Detector with Temporal Cue Inputs for Real-time Object Detection0
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