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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 191200 of 259 papers

TitleStatusHype
Edge Network-Assisted Real-Time Object Detection Framework for Autonomous Driving0
Object Detection in the Context of Mobile Augmented Reality0
Traffic Prediction Framework for OpenStreetMap using Deep Learning based Complex Event Processing and Open Traffic CamerasCode0
Real Time Multi-Class Object Detection and Recognition Using Vision Augmentation Algorithm0
AU-AIR: A Multi-modal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillance0
Real-Time Object Detection and Recognition on Low-Compute Humanoid Robots using Deep Learning0
An Aerial Weed Detection System for Green Onion Crops Using the You Only Look Once (YOLOv3) Deep Learning Algorithm0
Real-Time Object Detection and Localization in Compressive Sensed Video on Embedded Hardware0
Deep SCNN-based Real-time Object Detection for Self-driving Vehicles Using LiDAR Temporal Data0
SpineNet: Learning Scale-Permuted Backbone for Recognition and LocalizationCode0
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