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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 7180 of 259 papers

TitleStatusHype
CSPNet: A New Backbone that can Enhance Learning Capability of CNNCode1
CenterMask : Real-Time Anchor-Free Instance SegmentationCode1
HarDNet: A Low Memory Traffic NetworkCode1
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object DetectionCode1
MnasNet: Platform-Aware Neural Architecture Search for MobileCode1
Joint Anchor-Feature Refinement for Real-Time Accurate Object Detection in Images and VideosCode1
YOLOv3: An Incremental ImprovementCode1
Mask R-CNNCode1
YOLO9000: Better, Faster, StrongerCode1
You Only Look Once: Unified, Real-Time Object DetectionCode1
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