Development of the computer vision system based on machine learning for educational purposes

Authors

DOI:

https://doi.org/10.31812/educdim.4717

Keywords:

computer vision, COVID-19, mask detection, education

Abstract

The article provides an overview of the origins and current state of machine vision systems, examples of machine vision problems. The article describes the use of computer vision systems in education in both conventional and pandemic conditions. The COVID-19 pandemic has triggered changes in education that have modified existing educational applications of computer vision systems and spawned new ones, including social distancing, facial mask recognition, detection of infiltration into universities and schools, prevention of vandalism and detection of suspicious objects, attendance monitoring, recognition of emotions on faces in and without masks. Computer vision systems can also be used in education to introduce immersive educational resources. On the basis of the analysis of autonomous libraries for the identification of dynamic objects, it is concluded that in the creation of computer vision systems for educational purposes it is advisable to use computer vision libraries based on in-depth learning (in particular, the implementation of convolutional neural networks). A prototype computer vision system developed on the basis of Microsoft Cognitive Toolkit and deployed in the Microsoft Azure cloud is described. The system allows you to perform with a high degree of reliability the main functions: identification of emotions and the presence of a mask on the face, as well as allows you to determine sex, age, hair color, smile intensity, the presence of makeup, glasses, etc.

Downloads

Download data is not yet available.
Abstract views: 450 / PDF (Ukrainian) views: 75

References

Google Ngram Viewer. https://books.google.com/ngrams/graph?content=computer+vision%2C+machine+vision&year_start=1800&year_end=2019&corpus=26&smoothing=3&direct_url=t1%3B%2Ccomputer%20vision% 3B%2Cc0%3B.t1%3B%2Cmachine%20vision%3B%2Cc0#t1%3B%2Ccomputer%20vision%3B%2Cc0%3B.t1%3B%2Cmachine%20vision%3B%2Cc0

Adaptive Vision. Libraries comparison. https://docs.adaptive-vision.com/avl/technical_issues/LibrariesComparison.html

Lakshya Agarwal, Manan Mukim, Harish Sharma, Amit Bhandari, and Atul Mishra. 2021. Face Recognition Based Smart and Robust Attendance Monitoring using Deep CNN. In 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom). 699–704 (2021). doi: 10.1109/INDIACom51348.2021.00124

Dana, H. Ballard and Christopher M. Brown. Computer Vision. Prentice Hall, Englewood Cliffs. https://archive.org/details/computervision0000ball (1982). Accessed 13 Nov 2021

Bennett, J.: Happy, Sad, Angry Workshop. https://github.com/jimbobbennett/HappySadAngryWorkshop (2020). Accessed 13 Nov 2021

Gibson, J. J.: The Perception of the Visual World. Boston (1950) DOI: https://doi.org/10.2307/1418003 DOI: https://doi.org/10.2307/1418003

Google Cloud. Vision API Product Search pricing. https://cloud.google.com/vision/product-search/pricing48 (2021). Accessed 13 Nov 2021

Gunnar Rutger Grape. Model Based (Intermediate-Level) Computer Vision. Ph. D. Dissertation. Stanford University. https://apps.dtic.mil/sti/pdfs/AD0763673.pdf46 (1973). Accessed 13 Nov 2021

Klingler, N.: Top 8 Applications of Computer Vision in the Education Sector. https://viso.ai/applications/computer-vision-in-education (2021). Accessed 13 Nov 2021

Simon, J. D.: Prince. Computer Vision: Models, Learning, and Inference. Cambridge University Press (2012). Accessed 13 Nov 2021 DOI: https://doi.org/10.1017/CBO9780511996504

Juliet, R. C.: Pulliam, Cari van Schalkwyk, Nevashan Govender, Anne von Gottberg, Cheryl Cohen, Michelle J. Groome, Jonathan Dushoff, Koleka Mlisana, and Harry Moultrie. Increased risk of SARS-CoV-2 reinfection associated with emergence of the Omicron variant in South Africa. medRxiv (2021). doi: 10.1101/2021.11.11.21266068 DOI: https://doi.org/10.1101/2021.11.11.21266068 DOI: https://doi.org/10.1101/2021.11.11.21266068

Ashwin Raj, Aparna Raj, and Imteyaz Ahmad. Smart Attendance Monitoring System with Computer Vision Using IOT. Journal of Mobile Multimedia 17, 1–3, 115–125 (2021). doi: 10.13052/jmm1550-4646.17135 DOI: https://doi.org/10.13052/jmm1550-4646.17135 DOI: https://doi.org/10.13052/jmm1550-4646.17135

Rezaei, M., Azarmi, M.: 2020. DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic. Applied Sciences 10, 21 (2020). doi: 10.3390/app10217514 DOI: https://doi.org/10.3390/app10217514 DOI: https://doi.org/10.3390/app10217514

Lawrence, G. R.: Machine perception of three-dimensional solids. Thesis (Ph. D.). Massachusetts Institute of Technology. https://dspace.mit.edu/bitstream/handle/1721.1/11589/47 (1963). Accessed 13 Nov 2021

Shorten, C., Khoshgoftaar, N. M., Furht, B.: 2021. Deep Learning applications for COVID-19. Journal of Big Data 8 (1), (11 Jan 2021), 18 (2021). doi: 10.1186/s40537-020-00392-9 DOI: https://doi.org/10.1186/s40537-020-00392-9 DOI: https://doi.org/10.1186/s40537-020-00392-9

Sivakumar, S. A., John, J. T., Selvi, G. T., Madhu, B., Shankar, S. U, Arjun, K. P.: 2021. IoT based Intelligent Attendance Monitoring with Face Recognition Scheme. In 2021 5 th International Conference on Computing Methodologies and Communication (ICCMC). 349–353 (2021). doi: 10.1109/ ICCMC51019.2021.9418264 DOI: https://doi.org/10.1109/ICCMC51019.2021.9418264 DOI: https://doi.org/10.1109/ICCMC51019.2021.9418264

Ivan Edward Sutherland. Sketchpad, a man-machine graphical communication system. Ph. D. Dissertation. Massachusetts Institute of Technology. http://images.designworldonline.com.s3.amazonaws.com/CADhistory/Sketchpad_A_Man-Machine_Graphical_Communication_System_Jan63.pdf (1963). Accessed 13 Nov 2021 DOI: https://doi.org/10.1145/1461551.1461591 DOI: https://doi.org/10.1145/1461551.1461591

Tkachuk, V., Yechkalo, Yu., Semerikov, S., Kislova, M, Hladyr, Y.: Using Mobile ICT for Online Learning During COVID-19 Lockdown. In Information and Communication Technologies in Education, Research, and Industrial Applications, Andreas Bollin, Vadim Ermolayev, Heinrich C. Mayr, Mykola Nikitchenko, Aleksander Spivakovsky, Mykola Tkachuk, Vitaliy Yakovyna, and Grygoriy Zholtkevych (eds.). Springer International Publishing, Cham, 46–67 (2021). doi: 10.1007/978-3-030-77592-6_3 DOI: https://doi.org/10.1007/978-3-030-77592-6_3 DOI: https://doi.org/10.1007/978-3-030-77592-6_3

viso.ai. Abandoned Luggage. https://viso.ai/application/abandonedluggage-detection (2021). Accessed 13 Nov 2021

viso.ai. Face Recognition. https://viso.ai/application/face-recognition (2021). Accessed 13 Nov 2021

viso.ai. Facial Emotion Analysis. https://viso.ai/application/emotionanalysis (2021). Accessed 13 Nov 2021

viso.ai. Intrusion Detection. https://viso.ai/application/intrusion-detection (2021). Accessed 13 Nov 2021

viso.ai. Mask Detection: Automatically detect unmasked people in public spaces or indoors. https://viso.ai/application/mask-detection (2021). Accessed 13 Nov 2021

viso.ai. 2021. Parking Lot Occupancy. https://viso.ai/application/parking-lotoccupancy-detection (2021). Accessed 13 Nov 2021

viso.ai. Social Distancing Monitoring. https://viso.ai/application/social-distancing-monitoring (2021). Accessed 13 Nov 2021

Published

09-12-2021

Issue

Section

Articles

How to Cite

Semerikov, S.O., Vakaliuk, T.A., Mintii, I.S., Hamaniuk, V.A., Soloviev, V.N., Bondarenko, O.V., Nechypurenko, P.P., Shokaliuk, S.V., Moiseienko, N.V. and Ruban, V.R., 2021. Development of the computer vision system based on machine learning for educational purposes. Educational Dimension [Online], 5, pp.8–60. Available from: https://doi.org/10.31812/educdim.4717 [Accessed 8 December 2024].
Received 2021-10-31
Accepted 2021-12-01
Published 2021-12-09

Similar Articles

1-10 of 137

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)

1 2 3 4 > >>