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
- Muaayed F. AL-RAWI, CONVENTIONAL ENERGY EFFICIENT ROUTING PROTOCOLS IN WIRELESS SENSOR NETWORKS , Applied Computer Science: Vol. 16 No. 3 (2020)
- Arkadiusz GOLA, Łukasz WIECHETEK, MODELLING AND SIMULATION OF PRODUCTION FLOW IN JOB-SHOP PRODUCTION SYSTEM WITH ENTERPRISE DYNAMICS SOFTWARE , Applied Computer Science: Vol. 13 No. 4 (2017)
- Mahmoud BAKR, Sayed ABDEL-GABER, Mona NASR, Maryam HAZMAN, TOMATO DISEASE DETECTION MODEL BASED ON DENSENET AND TRANSFER LEARNING , Applied Computer Science: Vol. 18 No. 2 (2022)
- Qingyu Liu, Roben A. Juanatas, MASK FACE INPAINTING BASED ON IMPROVED GENERATIVE ADVERSARIAL NETWORK , Applied Computer Science: Vol. 19 No. 2 (2023)
- Grzegorz RADZKI, Amila THIBBOTUWAWA, Grzegorz BOCEWICZ, UAVS FLIGHT ROUTES OPTIMIZATION IN CHANGING WEATHER CONDITIONS – CONSTRAINT PROGRAMMING APPROACH , Applied Computer Science: Vol. 15 No. 3 (2019)
- Katarzyna GOSPODAREK, DETERMINATION OF RELATIVE LENGTHS OF BONE SEGMENTS OF THE DOMESTIC CAT'S LIMBS BASED ON THE DIGITAL IMAGE ANALYSIS , Applied Computer Science: Vol. 15 No. 2 (2019)
- ABDERRAHIM BAHANI, El Houssine Ech-Chhibat, Hassan SAMRI, Laila AIT MAALEM , Hicham AIT EL ATTAR , INTELLIGENT CONTROLLING THE GRIPPING FORCE OF AN OBJECT BY TWO COMPUTER-CONTROLLED COOPERATIVE ROBOTS , Applied Computer Science: Vol. 19 No. 1 (2023)
- Rawaa HAAMED, Ekhlas HAMEED, CONTROLLING THE MEAN ARTERIAL PRESSURE BY MODIFIED MODEL REFERENCE ADAPTIVE CONTROLLER BASED ON TWO OPTIMIZATION ALGORITHMS , Applied Computer Science: Vol. 16 No. 2 (2020)
- Wafaa Mustafa HAMEED, Asan Baker KANBAR, USING GA FOR EVOLVING WEIGHTS IN NEURAL NETWORKS , Applied Computer Science: Vol. 15 No. 3 (2019)
- Anitha Rani PALAKAYALA, Kuppusamy P, A QUALITATIVE AND QUANTITATIVE APPROACH USING MACHINE LEARNING AND NON-MOTOR SYMPTOMS FOR PARKINSON’S DISEASE CLASSIFICATION. A HIERARCHICAL STUDY , Applied Computer Science: Vol. 20 No. 3 (2024)
<< < 4 5 6 7 8 9 10 11 12 13 > >>
You may also start an advanced similarity search for this article.