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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 3140 of 259 papers

TitleStatusHype
Real-time Object Detection for Streaming PerceptionCode2
FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of VehiclesCode2
YOLOv5-6D: Advancing 6-DoF Instrument Pose Estimation in Variable X-Ray Imaging GeometriesCode2
Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged NetworksCode1
Non-deep NetworksCode1
MnasNet: Platform-Aware Neural Architecture Search for MobileCode1
Mining Negative Temporal Contexts For False Positive Suppression In Real-Time Ultrasound Lesion DetectionCode1
MODIPHY: Multimodal Obscured Detection for IoT using PHantom Convolution-Enabled Faster YOLOCode1
Object Detection and Pose Estimation from RGB and Depth Data for Real-time, Adaptive Robotic GraspingCode1
HIC-YOLOv5: Improved YOLOv5 For Small Object DetectionCode1
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