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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 131140 of 259 papers

TitleStatusHype
Real-Time Indoor Object Detection based on hybrid CNN-Transformer Approach0
Multiple receptive fields and small-object-focusing weakly-supervised segmentation network for fast object detection0
Efficient Object Detection Model for Real-Time UAV Applications0
A Proposed Artificial intelligence Model for Real-Time Human Action Localization and Tracking0
2D Object Detection: A Survey0
3D-FCT: Simultaneous 3D Object Detection and Tracking Using Feature Correlation0
A 58.6mW Real-Time Programmable Object Detector with Multi-Scale Multi-Object Support Using Deformable Parts Model on 1920x1080 Video at 30fps0
Achieving Real-Time Object Detection on MobileDevices with Neural Pruning Search0
A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS0
A Comprehensive Study of Real-Time Object Detection Networks Across Multiple Domains: A Survey0
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