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
Statistics
Abstract views: 509PDF downloads: 90
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Most read articles by the same author(s)
- Mohanad ABDULHAMID, Deng PETER, REMOTE HEALTH MONITORING: FALL DETECTION , Applied Computer Science: Vol. 16 No. 1 (2020)
- Mohanad ABDULHAMID, Njagi KINYUA, SOFTWARE FOR RECOGNITION OF CAR NUMBER PLATE , Applied Computer Science: Vol. 16 No. 1 (2020)
Similar Articles
- Elizabeth Perez, Juan A. Araiza, Dreysy Pozos, Edmundo Bonilla, Jose C. Hernandez, Jesus A. Cortes, APPLICATION FOR FUNCTIONALITY AND REGISTRATION IN THE CLOUD OF A MICROCONTROLLER DEVELOPMENT BOARD FOR IOT IN AWS , Applied Computer Science: Vol. 17 No. 2 (2021)
- Damian KOLNY, Dorota WIĘCEK, Paweł ZIOBRO, Martin KRAJČOVIČ, APPLICATION OF A COMPUTER TOOL MONITORING SYSTEM IN CNC MACHINING CENTRES , Applied Computer Science: Vol. 13 No. 4 (2017)
- Kusay F. AL-TABATABAIE, Sadeer D. ABDULAMEER, APPLYING ARDUINO FOR CONTROLLING CAR PARKING SYSTEM , Applied Computer Science: Vol. 15 No. 2 (2019)
- Marcin TOMCZYK, Barbara BOROWIK, Mariusz MIKULSKI, IDENTIFICATION OF A BACKLASH ZONE IN AN ELECTROMECHANICAL SYSTEM CONTAINING CHANGES OF A MASS INERTIA MOMENT BASED ON A WAVELET–NEURAL METHOD , Applied Computer Science: Vol. 14 No. 4 (2018)
- Marcin TOMCZYK, Anna PLICHTA, Mariusz MIKULSKI, APPLICATION OF IMAGE ANALYSIS TO THE IDENTIFICATION OF MASS INERTIA MOMENTUM IN ELECTROMECHANICAL SYSTEM WITH CHANGEABLE BACKLASH ZONE , Applied Computer Science: Vol. 15 No. 3 (2019)
- Nasir A. Al-Awad, Izz K. Abboud, Muaayed F. Al-Rawi, GENETIC ALGORITHM-PID CONTROLLER FOR MODEL ORDER REDUCTION PANTOGRAPHCATENARY SYSTEM , Applied Computer Science: Vol. 17 No. 2 (2021)
- Manikandan SRIDHARAN, Delphin Carolina RANI ARULANANDAM, Rajeswari K CHINNASAMY, Suma THIMMANNA, Sivabalaselvamani DHANDAPANI, RECOGNITION OF FONT AND TAMIL LETTER IN IMAGES USING DEEP LEARNING , Applied Computer Science: Vol. 17 No. 2 (2021)
- Saha RENO, Sheikh Surfuddin Reza Ali CHOWDHURY, Iqramuzzaman SADI, MITIGATING LOAN ASSOCIATED FINANCIAL RISK USING BLOCKCHAIN BASED LENDING SYSTEM , Applied Computer Science: Vol. 17 No. 2 (2021)
- Sylwester KORGA, Marcin BARSZCZ, Krzysztof DZIEDZIC, DEVELOPMENT OF SOFTWARE FOR IDENTIFICATION OF FILAMENTS USED IN 3D PRINTING TECHNOLOGY , Applied Computer Science: Vol. 15 No. 1 (2019)
- Shadan Mohammed Jihad ABDALWAHID, Raghad Zuhair YOUSIF, Shahab Wahhab KAREEM, ENHANCING APPROACH USING HYBRID PAILLER AND RSA FOR INFORMATION SECURITY IN BIGDATA , Applied Computer Science: Vol. 15 No. 4 (2019)
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
