Modification of the Method of Object Contours Extraction in Intelligent Systems
Alexey Ivanovich MARTYSHKIN,
Elena Grigorievna BERSHADSKAYA,
Evgeny Igorevich MARKIN,
Valentina Vladimirovna ZUPAROVA https://doi.org/10.15514/ISPRAS-2022-34(6)-9
Abstract
This research paper focuses on the use of computer vision in intelligent systems to analyze human contours. With the growth of technology in various industries, there is a need to improve the efficiency of human-computer systems. The proposed method uses a video camera and computer software to detect a person in the image and process it using the OpenCV library and C++ programming language. The paper reviews existing human detection methods, analyzes an alternative method that uses computer vision, and develops a new method for human detection. Modifications include the use of the Kuwahar filter for image blurring and the Sobel algorithm for outline extraction. Applications for this technology include security at transportation hubs and crowded areas, remote health monitoring, enhanced control at borders and secure facilities, and interactive advertising and entertainment.
About the Authors
Alexey Ivanovich MARTYSHKIN
Penza State Technological University
Russian Federation
Russian Federation
Candidate of Technical Sciences, Associate Professor, Head of the Department of Programming
Elena Grigorievna BERSHADSKAYA
Penza State Technological University
Russian Federation
Russian Federation
Candidate of Technical Sciences, Professor, Professor of the Department of Programming
Evgeny Igorevich MARKIN
Penza State Technological University
Russian Federation
Russian Federation
Candidate of Technical Sciences, Assistant of the Department of Programming
Valentina Vladimirovna ZUPAROVA
Penza State Technological University
Russian Federation
Russian Federation
Postgraduate Student of the Department of Programming
References
1. Gregor K., Besse F. Self-Organizing Intelligent Matter: A blueprint for an AI generating algorithm. arXiv2101.07627, 2021, 13 p.
2. Yun J., Lee S. Human Movement Detection and Identification Using Pyroelectric Infrared Sensors. Sensors. vol. 14, issue 5, 2014, pp. 8057–8081
3. Imano W., Kameyama K. et al. Non-Contact Respiratory Measurement Using a Depth Camera for Elderly People. Sensors, vol. 20, issue 23, 2020, pp. 1–12
4. Бершадская Е.Г., Маркин Е.И., Мартышкин А.И. Методы идентификации личности по изображению лица. XXI век итоги прошлого и проблемы настоящего плюс, том 9, вып. 1, стр. 49-53 / Bershadskaya E.G., Markin E.I., Martyshkin A.I. Methods for personal image identification. XXI Century: Resumes of the Past and Challenges of the Present plus, vol. 9, issue 1, pp. 49-53 (in Russian).
5. Mittal M., Verma A. et al. An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis. IEEE Access, vol. 7, pp. 33240-33255
6. Jain A., Gupta R. Gaussian filter threshold modulation for filtering flat and texture area of an image. In Proc. of the International Conference on Advances in Computer Engineering and Applications, 2015, pp. 760-763
7. Ramadhan A., Mahmood F., Elci A. Image denoising by median filter in wavelet domain. International Journal of Multimedia & Its Applications (IJMA), vol.9, issue 1, 2017, pp. 31-40.
8. Xu Z., Baojie X., Guoxin W. Canny edge detection based on Open CV. In Proc. of the 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), 2017, pp. 53-56.
9. Vliet L.J., Young I.T., Beckers G.L. A nonlinear laplace operator as edge detector in noisy images. Computer Vision, Graphics, and Image Processing, vol. 45, issue 2, 1989, pp. 167-195
10. Bansal R., Raj G., Choudhury T. Blur image detection using Laplacian operator and Open-CV. In Proc. of the International Conference on System Modeling & Advancement in Research Trends (SMART), Moradabad, India, 2016, pp. 63-67
11. Damanik R.R., Sitanggang D. et al. An application of viola jones method for face recognition for absence process efficiency. Journal of Physics: Conference Series, vol. 1007, 2018, article id. 012013, 8 p.
12. Shakil S., Lee C., Keilholz S.D. Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states. NeuroImage, vol. 133, 2016, pp. 111-128.
13. Ma S. Bai L. A face detection algorithm based on Adaboost and new Haar-Like feature. In Proc. of the 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), 2016, pp. 651-654.
14. Sun X., Wu P., Hoi C.H. Face detection using deep learning: An improved faster RCNN approach. Neurocomputing, vol. 299, 2018, pp. 42-50.
15. Singh A., Herunde H., Furtado F. Modified Haar-cascade model for face detection issues. International Journal of Research in Industrial Engineering, vol. 9, issue 2, 2022, pp.143-171.
16. Eng S. K., Ali H. et al. Facial expression recognition in JAFFE and KDEF Datasets using histogram of oriented gradients and support vector machine. IOP Conference Series: Materials Science and Engineering, vol. 705, 2019, article id. 012031, 7 p.
17. Biswas S., Ghoshal D. A model of noise reduction using Gabor Kuwahara filter. In Proc. of the 4th International Conference on Advanced Computing and Communication Systems (ICACCS), 2017, pp. 1-5
18. Lang Y., Zheng D. An Improved Sobel Edge Detection Operator. In Proc. of the 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016), 2016, pp. 590-593.
Review
For citations:
MARTYSHKIN A.I., BERSHADSKAYA E.G., MARKIN E.I., ZUPAROVA V.V. Modification of the Method of Object Contours Extraction in Intelligent Systems. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2022;34(6):127-136. (In Russ.) https://doi.org/10.15514/ISPRAS-2022-34(6)-9
Email this article 