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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 5160 of 259 papers

TitleStatusHype
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object DetectionCode1
Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged NetworksCode1
Mining Negative Temporal Contexts For False Positive Suppression In Real-Time Ultrasound Lesion DetectionCode1
RegionCLIP: Region-based Language-Image PretrainingCode1
CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing ImagesCode1
MnasNet: Platform-Aware Neural Architecture Search for MobileCode1
CenterMask : Real-Time Anchor-Free Instance SegmentationCode1
CBNet: A Composite Backbone Network Architecture for Object DetectionCode1
MODIPHY: Multimodal Obscured Detection for IoT using PHantom Convolution-Enabled Faster YOLOCode1
Optimizing Edge Offloading Decisions for Object DetectionCode1
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