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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 161170 of 259 papers

TitleStatusHype
CorrDiff: Adaptive Delay-aware Detector with Temporal Cue Inputs for Real-time Object Detection0
Dataset and Benchmarking of Real-Time Embedded Object Detection for RoboCup SSL0
DeepLOC: Deep Learning-based Bone Pathology Localization and Classification in Wrist X-ray Images0
Deep SCNN-based Real-time Object Detection for Self-driving Vehicles Using LiDAR Temporal Data0
Deriving star cluster parameters with convolutional neural networks. II. Extinction and cluster/background classification0
A Deep Learning Framework for Detection of Targets in Thermal Images to Improve Firefighting0
Detection of Rail Line Track and Human Beings Near the Track to Avoid Accidents0
DS MYOLO: A Reliable Object Detector Based on SSMs for Driving Scenarios0
Dynamic Zoom-in Network for Fast Object Detection in Large Images0
Edge Network-Assisted Real-Time Object Detection Framework for Autonomous Driving0
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