Computer vision based on Raspberry Pi system

• Autorzy: Abdulhamid Mohanad , Odondi Otieno , Al-Rawi Muaayed

Identyfikatory

DOI

10.23743/acs-2020-31

• Języki publikacji: EN

Abstrakty

EN

The paper focused on designing and developing a Raspberry Pi based system employing a camera which is able to detect and count objects within a target area. Python was the programming language of choice for this work. This is because it is a very powerful language, and it is compatible with the Pi. Besides, it lends itself to rapid application development and there are online communities that program Raspberry Pi computer using Python. The results show that the implemented system was able to detect different kinds of objects in a given image. The number of objects were also generated displayed by the system. Also the results show an average efficiency of 90.206 % was determined. The system is therefore seen to be highly reliable.

Słowa kluczowe

EN

computer vision; raspberry Pi system

PL

system wizyjny; Raspberry Pi

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2020
• Tom: Vol. 16, no. 4
• Strony: 85--102
• Opis fizyczny: Bibliogr. 6 poz., fig., tab.

Twórcy

autor

Abdulhamid Mohanad

• Al-Hikma University, Karada Kharidge, Baghdad, Iraq

autor

Odondi Otieno

• University of Nairobi, P.O.Box 30197, GPO, Nairobi, Kenya

autor

Al-Rawi Muaayed

• AL-Mustansiryia University, Baghdad, Iraq

Bibliografia

• [1] Islam, M. M., Azad, M. S. U., Alam, M. A., & Hassan, A. (2014). Raspberry Pi and image processing based Electronic Voting Machine (EVM). International Journal of Scientific and Engineering Research, 5(1), 1506–1510.
• [2] Jana, S., & Borkar, S. (2017). Autonomous object detection and tracking using Raspberry Pi. International Journal of Engineering Science and Computing, 7(7), 14151–14155.
• [3] Nikam, A., Doddamani, A., Deshpande, D., & Manjramkar, S. (2017). Raspberry Pi Based obstacle avoiding robot. International Research Journal of Engineering and Technology, 4(2), 917–919.
• [4] Odondi, O. (2016). Computer Vision through the Raspberry-PI: Counting Objects (graduation project). University of Nairobi, Kenya.
• [5] Sandin, V. (2017). Object detection and analysis using computer vision (graduation project). Chalmers University of Technology, Sweden.
• [6] Senthilkumar, G., Gopalakrishnan, K., & Sathish Kumar, V. (2014). Embedded image capturing system using Raspberry Pi system. International Journal of Emerging Trends and Technology in Computer Science, 3(2), 213–215.