NDLI: Implementation of Embedded ARM9 Platform using Qt And openCV For Human Upper Body Detection

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Implementation of Embedded ARM9 Platform using Qt And openCV For Human Upper Body Detection

Content Provider	Semantic Scholar
Author	Patel, Krunal Jadav, Kalpesh R.
Copyright Year	2014
Abstract	In this Paper, A novel architecture for automotive vision using an embedded device will be implemented on ARM9 Board with highly computing capabilities and low processing power. Currently, achieving real-time image processing routines such as convolution, thresholding, edge detection and some of the complex media applications is a challenging task in embedded Device, because of limited memory. An open software framework, Linux OS is used in embedded devices to provide a good starting point for developing the multitasking kernel, integrated with communication protocols, data management and graphical user interface for reducing the total development time. To resolve the problems faced by the image processing applications in embedded Device a new application environment was developed. This environment provides the resources available in the operating system which runs on the hardware with complex image processing libraries. This paper presents the capture of an image from the USB camera, applied to image processing algorithms to Detect Human Upper Body. The application (GUI) Graphical User Interface was designed using Qt and ARM Linux gcc Integrated Development Environment (IDE) for implementing image processing algorithm using Open Source Computer Vision Library (OpenCV). This developed software integrated in mobiles by the cross compilation of Qt and the OpenCV software for Linux Operating system. The result utilized by Viola and Jones Algorithm with Haar Features of the image using OpenCV.
Starting Page	73
Ending Page	79
Page Count	7
File Format	PDF HTM / HTML
Volume Number	9
Alternate Webpage(s)	http://iosrjournals.org/iosr-jece/papers/Vol9-Issue2/Version-2/L09227379.pdf
Alternate Webpage(s)	https://doi.org/10.9790/2834-09227379
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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