ACS Applied Computer Science

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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.

  • APA 6th style
Abdulhamid, M., Odondi, O., & Al-Rawi, M. (2020). Computer vision based on Raspberry Pi system. Applied Computer Science, 16(4), 85-102. doi:10.23743/acs-2020-31
  • Chicago style
Abdulhamid, Mohanad, Otieno Odondi, and Muaayed Al-Rawi. "Computer Vision Based on Raspberry Pi System." Applied Computer Science 16, no. 4 (2020): 85-102.
  • IEEE style
M. Abdulhamid, O. Odondi, and M. Al-Rawi, "Computer vision based on Raspberry Pi system," Applied Computer Science, vol. 16, no. 4, pp. 85-102, 2020, doi: 10.23743/acs-2020-31.
  • Vancouver style
Abdulhamid M, Odondi O, Al-Rawi M. Computer vision based on Raspberry Pi system. Applied Computer Science. 2020;16(4):85-102.