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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 91100 of 259 papers

TitleStatusHype
ATLASv2: LLM-Guided Adaptive Landmark Acquisition and Navigation on the Edge0
Liquid Leak Detection Using Thermal Images0
DeepLOC: Deep Learning-based Bone Pathology Localization and Classification in Wrist X-ray Images0
Dataset and Benchmarking of Real-Time Embedded Object Detection for RoboCup SSL0
Asymmetric Pruning for Learning Cascade Detectors0
A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets0
ARTOS -- Adaptive Real-Time Object Detection System0
A Comprehensive Study of Real-Time Object Detection Networks Across Multiple Domains: A Survey0
2D Object Detection: A Survey0
Globally-scalable Automated Target Recognition (GATR)0
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