NDLI: Design of vision-based indoor positioning based on embedded system

Content Provider	IET Digital Library
Author	Tsai, Tsung Han Chang, Chih Hao Chen, Shih Wei Yao, Chia Hsiang
Abstract	Indoor positioning techniques have become very important in recent years. Due to the wide deployment of surveillance cameras, it has become feasible to use the videos for indoor positioning. The success of using this approach can also reduce the load of security persons of watching the monitors all the time. In this study, the authors propose a vision-based indoor positioning system. The proposed method uses a frame processing technique and applies the Gaussian mixture learning for video background model. The foreground object can be extracted by using the background subtraction. Based on the foreground object, the objects can be tracked and used in the direct linear transform, and generate a bird's-eye map with camera information. A real-time demonstration has been also provided. It shows the tracing of the moving objects and the bird's-eye view.
Starting Page	423
Ending Page	430
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 3, Feb (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6285
Journal	IET Image Processing
Publisher Date	2019-08-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Camera Computer Vision Computer Vision And Image Processing Technique Embedded System Foreground Object Frame Processing Technique Gaussian Mixture Learning Gaussian Processes Image Motion Analysis Indoor Positioning Technique Indoor Radio Knowledge Engineering Technique Learning in AI Monitors All The Time Object Detection Optical, Image And Video Signal Processing Security Persons Statistics Surveillance Surveillance Cameras Video Background Model Video Signal Processing Video Surveillance Vision-based Indoor Positioning System
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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