Video surveillance using raspberry PI GPU

Today, surveillance system is being utilized and deployed in many places to provide supervision and bring security to people. The most commonly used technology currently is Closed-Circuit Television (CCTV). However, there are several defects with the technology such as anomalies cannot be identified...

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主要作者: Wong, Yan Yin
格式: Thesis
語言:English
出版: 2018
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在線閱讀:http://eprints.utm.my/id/eprint/79581/1/WongYanYinPFKE2018.pdf
http://eprints.utm.my/id/eprint/79581/
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spelling my.utm.795812018-10-31T13:00:14Z http://eprints.utm.my/id/eprint/79581/ Video surveillance using raspberry PI GPU Wong, Yan Yin TK Electrical engineering. Electronics Nuclear engineering Today, surveillance system is being utilized and deployed in many places to provide supervision and bring security to people. The most commonly used technology currently is Closed-Circuit Television (CCTV). However, there are several defects with the technology such as anomalies cannot be identified automatically and expensive. This project proposed to use the GPU in Raspberry Pi for video surveillance task. Raspberry Pi is a powerful single-board computer which features an ARM processor and a VideoCore IV graphics processing unit (GPU). It is sufficiently powerful to work as a video surveillance system and relatively cheap compared to CCTV. Furthermore, GPU is optimized for parallel computing of video data. It can theoretically provides better performance and have higher efficiency in video processing compared to CPU. Hence, the GPU in Raspberry Pi should provides large performance gain by porting the algorithm from CPU-only reference to works on GPU. The objective of this project is to explore on how the GPU can be programmed for the purpose of video surveillance. 2018 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/79581/1/WongYanYinPFKE2018.pdf Wong, Yan Yin (2018) Video surveillance using raspberry PI GPU. Masters thesis, Universiti Teknologi Malaysia, Faculty of Electrical Engineering.
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Wong, Yan Yin
Video surveillance using raspberry PI GPU
description Today, surveillance system is being utilized and deployed in many places to provide supervision and bring security to people. The most commonly used technology currently is Closed-Circuit Television (CCTV). However, there are several defects with the technology such as anomalies cannot be identified automatically and expensive. This project proposed to use the GPU in Raspberry Pi for video surveillance task. Raspberry Pi is a powerful single-board computer which features an ARM processor and a VideoCore IV graphics processing unit (GPU). It is sufficiently powerful to work as a video surveillance system and relatively cheap compared to CCTV. Furthermore, GPU is optimized for parallel computing of video data. It can theoretically provides better performance and have higher efficiency in video processing compared to CPU. Hence, the GPU in Raspberry Pi should provides large performance gain by porting the algorithm from CPU-only reference to works on GPU. The objective of this project is to explore on how the GPU can be programmed for the purpose of video surveillance.
format Thesis
author Wong, Yan Yin
author_facet Wong, Yan Yin
author_sort Wong, Yan Yin
title Video surveillance using raspberry PI GPU
title_short Video surveillance using raspberry PI GPU
title_full Video surveillance using raspberry PI GPU
title_fullStr Video surveillance using raspberry PI GPU
title_full_unstemmed Video surveillance using raspberry PI GPU
title_sort video surveillance using raspberry pi gpu
publishDate 2018
url http://eprints.utm.my/id/eprint/79581/1/WongYanYinPFKE2018.pdf
http://eprints.utm.my/id/eprint/79581/
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score 13.153044