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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 141150 of 259 papers

TitleStatusHype
A Convolutional Neural Network based Live Object Recognition System as Blind Aid0
Adaptive Object Detection for Indoor Navigation Assistance: A Performance Evaluation of Real-Time Algorithms0
A Decade of You Only Look Once (YOLO) for Object Detection0
A Fast HOG Descriptor Using Lookup Table and Integral Image0
An Efficient Real-Time Object Detection Framework on Resource-Constricted Hardware Devices via Software and Hardware Co-design0
An Aerial Weed Detection System for Green Onion Crops Using the You Only Look Once (YOLOv3) Deep Learning Algorithm0
A Review of YOLOv12: Attention-Based Enhancements vs. Previous Versions0
Artificial Intelligence Enabled Traffic Monitoring System0
ARTOS -- Adaptive Real-Time Object Detection System0
A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets0
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