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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 201210 of 259 papers

TitleStatusHype
Lidar based Detection and Classification of Pedestrians and Vehicles Using Machine Learning Methods0
DiCENet: Dimension-wise Convolutions for Efficient NetworksCode0
Efficient Object Detection Model for Real-Time UAV Applications0
Object Detection in Specific Traffic Scenes using YOLOv20
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object DetectionCode1
Multiple receptive fields and small-object-focusing weakly-supervised segmentation network for fast object detection0
CornerNet-Lite: Efficient Keypoint Based Object DetectionCode0
Fast object detection in compressed JPEG ImagesCode0
Objects as PointsCode2
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object DetectionCode0
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