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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
YOLO-Drone:Airborne real-time detection of dense small objects from high-altitude perspective0
A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS0
Real-time SLAM Pipeline in Dynamics Environment0
R-TOSS: A Framework for Real-Time Object Detection using Semi-Structured Pruning0
Help the Blind See: Assistance for the Visually Impaired through Augmented Acoustic SimulationCode0
DaDe: Delay-adaptive Detector for Streaming PerceptionCode0
Indian Commercial Truck License Plate Detection and Recognition for Weighbridge Automation0
ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy0
Automatic Cattle Identification using YOLOv5 and Mosaic Augmentation: A Comparative Analysis0
Self-Configurable Stabilized Real-Time Detection Learning for Autonomous Driving Applications0
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