COMPUTER VISION BASED ON RASPBERRY PI SYSTEM
Mohanad ABDULHAMID
moh1hamid@yahoo.comAl-Hikma University, Karada Kharidge, Baghdad (Iraq)
Otieno ODONDI
University of Nairobi, P.O.Box 30197, GPO, Nairobi (Kenya)
Muaayed AL-RAWI
AL-Mustansiryia University, Baghdad (Iraq)
Abstract
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.
Keywords:
Computer vision, Raspberry Pi systemReferences
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.
Google Scholar
Jana, S., & Borkar, S. (2017). Autonomous object detection and tracking using Raspberry Pi. International Journal of Engineering Science and Computing, 7(7), 14151–14155.
Google Scholar
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.
Google Scholar
Odondi, O. (2016). Computer Vision through the Raspberry-PI: Counting Objects (graduation project). University of Nairobi, Kenya.
Google Scholar
Sandin, V. (2017). Object detection and analysis using computer vision (graduation project). Chalmers University of Technology, Sweden.
Google Scholar
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.
Google Scholar
Authors
Otieno ODONDIUniversity of Nairobi, P.O.Box 30197, GPO, Nairobi Kenya
Authors
Muaayed AL-RAWIAL-Mustansiryia University, Baghdad Iraq
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