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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 151160 of 259 papers

TitleStatusHype
GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning0
Globally-scalable Automated Target Recognition (GATR)0
HGO-YOLO: Advancing Anomaly Behavior Detection with Hierarchical Features and Lightweight Optimized Detection0
Highly Scalable, Parallel and Distributed AdaBoost Algorithm using Light Weight Threads and Web Services on a Network of Multi-Core Machines0
How Real is CARLAs Dynamic Vision Sensor? A Study on the Sim-to-Real Gap in Traffic Object Detection0
Human-in-the-loop Reasoning For Traffic Sign Detection: Collaborative Approach Yolo With Video-llava0
Image Models for large-scale Object Detection and Classification0
Inception-YOLO: Computational cost and accuracy improvement of the YOLOv5 model based on employing modified CSP, SPPF, and inception modules0
Indian Commercial Truck License Plate Detection and Recognition for Weighbridge Automation0
Industrial object, machine part and defect recognition towards fully automated industrial monitoring employing deep learning. The case of multilevel VGG190
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