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
- Michał TOMCZYK, Anna PLICHTA, Mariusz MIKULSKI, APPLICATION OF WAVELET – NEURAL METHOD TO DETECT BACKLASH ZONE IN ELECTROMECHANICAL SYSTEMS GENERATING NOISES , Applied Computer Science: Vol. 15 No. 4 (2019)
- Thanh-Nghia NGUYEN, Thanh-Hai NGUYEN, Ba-Viet NGO, R PEAK DETERMINATION USING A WDFR ALGORITHM AND ADAPTIVE THRESHOLD , Applied Computer Science: Vol. 18 No. 3 (2022)
- Rumesh Edirimanne, W Madushan Fernando, Peter Nielsen, H. Niles Perera, Amila Thibbotuwawa, OPTIMIZING UNMANNED AERIAL VEHICLE BASED FOOD DELIVERY THROUGH VEHICLE ROUTING PROBLEM: A COMPARATIVE ANALYSIS OF THREE DELIVERY SYSTEMS. , Applied Computer Science: Vol. 20 No. 1 (2024)
- Waldemar SUSZYŃSKI, Małgorzata CHARYTANOWICZ, Wojciech ROSA, Leopold KOCZAN, Rafał STĘGIERSKI, DETECTION OF FILLERS IN THE SPEECH BY PEOPLE WHO STUTTER , Applied Computer Science: Vol. 17 No. 4 (2021)
- Ali Fattah DAKHIL, Weffa Muhammed ALI, Ali Atshan Abdul REDA, PRIORITIZING SOFTWARE CAPABILITIES AND FOCAL POINTS OF MS ACCESS AND EXCEL IN PERSPECTIVE OF DATA MANAGEMENT , Applied Computer Science: Vol. 14 No. 3 (2018)
- Sebastian CYGAN, Barbara BOROWIK, Bohdan BOROWIK, STREET LIGHTS INTELLIGENT SYSTEM, BASED ON THE INTERNET OF THINGS CONCEPT , Applied Computer Science: Vol. 14 No. 1 (2018)
- Fernando Andrés CEVALLOS SALAS, DIGITAL NEWS CLASSIFICATION AND PUNCTUACTION USING MACHINE LEARNING AND TEXT MINING TECHNIQUES , Applied Computer Science: Vol. 20 No. 2 (2024)
- Daniel HALIKOWSKI, Justyna PATALAS-MALISZEWSKA, Małgorzata SKRZESZEWSKA, A MODEL FOR ASSESSING THE LEVEL OF AUTOMATION OF A MAINTENANCE DEPARTMENT USING ARTIFICIAL NEURAL NETWORK , Applied Computer Science: Vol. 14 No. 4 (2018)
- Rosa Maria VAZQUEZ, Edmundo BONILLA, Eduardo SANCHEZ, Oscar ATRIANO, Cinthya BERRUECOS, APPLICATION OF DATA MINING TECHNIQUES TO FIND RELATIONSHIPS BETWEEN THE DISHES OFFERED BY A RESTAURANT FOR THE ELABORATION OF COMBOS BASED ON THE PREFERENCES OF THE DINERS , Applied Computer Science: Vol. 15 No. 2 (2019)
- Rowell HERNANDEZ, Robert ATIENZA, CAREER TRACK PREDICTION USING DEEP LEARNING MODEL BASED ON DISCRETE SERIES OF QUANTITATIVE CLASSIFICATION , Applied Computer Science: Vol. 17 No. 4 (2021)
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.