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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

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Showing 151–175 of 259 papers

TitleStatusHype
Contour Proposal Networks for Biomedical Instance SegmentationCode1
Swin Transformer: Hierarchical Vision Transformer using Shifted WindowsCode2
USB: Universal-Scale Object Detection Benchmark—0
Object Detection and Pose Estimation from RGB and Depth Data for Real-time, Adaptive Robotic GraspingCode1
CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing ImagesCode1
Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object DetectionCode1
Screening COVID-19 cases using Deep Neural Networks with X-ray images—0
Industrial object, machine part and defect recognition towards fully automated industrial monitoring employing deep learning. The case of multilevel VGG19—0
Modality-Buffet for Real-Time Object Detection—0
Scaled-YOLOv4: Scaling Cross Stage Partial NetworkCode1
Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep LearningCode1
Real-time object detection method based on improved YOLOv4-tinyCode0
Fast Object Detection with Latticed Multi-Scale Feature Fusion—0
R-TOD: Real-Time Object Detector with Minimized End-to-End Delay for Autonomous DrivingCode0
Deformable DETR: Deformable Transformers for End-to-End Object DetectionCode3
Artificial Intelligence Enabled Traffic Monitoring System—0
Robust and Efficient Post-Processing for Video Object Detection (REPP)Code1
YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-DesignCode1
Globally-scalable Automated Target Recognition (GATR)—0
Edge Network-Assisted Real-Time Object Detection Framework for Autonomous Driving—0
Object Detection in the Context of Mobile Augmented Reality—0
Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged NetworksCode1
Traffic Prediction Framework for OpenStreetMap using Deep Learning based Complex Event Processing and Open Traffic CamerasCode0
Understanding Object Detection Through An Adversarial LensCode1
End-to-End Object Detection with TransformersCode1
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