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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 221230 of 259 papers

TitleStatusHype
Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approach0
Real-time object detection and tracking using flash LiDAR imagery0
Real-Time Object Detection in Occluded Environment with Background Cluttering Effects Using Deep Learning0
Real-time Object Detection: YOLOv1 Re-Implementation in PyTorch0
Real-Time Onboard Object Detection for Augmented Reality: Enhancing Head-Mounted Display with YOLOv80
Real-time SLAM Pipeline in Dynamics Environment0
Real-time Traffic Object Detection for Autonomous Driving0
RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection0
ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy0
R-TOSS: A Framework for Real-Time Object Detection using Semi-Structured Pruning0
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