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- Author: Mark Collins
- Publisher: GRIN Verlag
- Year: 2014
- Pages: 70
- ISBN-10: 3656680744
- ISBN-13: 9783656680741
- Format: 14.8 x 21 x 0.4 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
Employment of low-cost low-power ARM machines as tracking device for real time vehicle movement (e-book) (used book) | bookbook.eu
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Bachelor Thesis from the year 2013 in the subject Computer Science - Technical Computer Science, grade: 69, University of Lincoln (School of Computer Science), course: Computer Science, language: English, abstract: This undergraduate Bachelor Thesis examines the use of a raspberry pi towards a real-time computer vision system. Advances in technology in recent years have steadily increased computational performance, ushering in the availability of affordable powerful, single board micro systems. This project attempts to showcase an application of low cost hardware for performing modern computer vision algorithms, paired with imaging sensors to emulate an embedded system. In order to achieve this goal, the project must demonstrate background learning, object detection, and establish methods for monitoring the real time movement of pedestrians and vehicles on a road. The implementation will make use of a Raspberry Pi type Model B as the main piece of computational hardware to be employed to an IP camera.
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- Author: Mark Collins
- Publisher: GRIN Verlag
- Year: 2014
- Pages: 70
- ISBN-10: 3656680744
- ISBN-13: 9783656680741
- Format: 14.8 x 21 x 0.4 cm, softcover
- Language: English English
Bachelor Thesis from the year 2013 in the subject Computer Science - Technical Computer Science, grade: 69, University of Lincoln (School of Computer Science), course: Computer Science, language: English, abstract: This undergraduate Bachelor Thesis examines the use of a raspberry pi towards a real-time computer vision system. Advances in technology in recent years have steadily increased computational performance, ushering in the availability of affordable powerful, single board micro systems. This project attempts to showcase an application of low cost hardware for performing modern computer vision algorithms, paired with imaging sensors to emulate an embedded system. In order to achieve this goal, the project must demonstrate background learning, object detection, and establish methods for monitoring the real time movement of pedestrians and vehicles on a road. The implementation will make use of a Raspberry Pi type Model B as the main piece of computational hardware to be employed to an IP camera.
Reviews