1. ACPS
  2. Всі випуски
  3. Випуск 7, Номер 2, 2022
  4. Software System for End-Products Accounting in Bakery Production Lines Based on Distributed Video Streams Analysis

Software System for End-Products Accounting in Bakery Production Lines Based on Distributed Video Streams Analysis

ACPS.
2022;
Випуск 7, Номер 2
: cc. 101 - 107
https://doi.org/10.23939/acps2022.02.101
Автори:
  1. Yurii Ivanov
  2. Borys Sharov
  3. Nazar Zalevskyi
  4. Ostap Kernytskyi
1
Lviv Polytechnic National University, Ukraine
2
Ph.D. and director of "Electronic Systems" Co. Ltd.
3
Lviv Polytechnic National University, Ukraine
4
Lviv Polytechnic National University, Ukraine

Among the main requirements of modern surveillance systems are stability in the face of negative influences and intellectualization. The purpose of intellectualization is that the surveillance system should perform not only the main functions such as monitoring and stream recording but also have to provide effective stream processing. The requirement for this processing is that the system operation has to be automated, and the operator's influence should be minimal. Modern intelligent surveillance systems require the development of grouping methods. The context of the grouping method here is associated with a decomposition of the target problem. Depending on the purpose of the system, the target problem can represent several subproblems, each of which usually accomplishes by artificial intelligence or data mining methods.

centroid
морфологія
сегментація
tracking
відеозображення
  1. Shi, H., Liu, C. (2018). “A New Global Foreground Modeling and Local Background Modeling Method for Video Analysis”, Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science, vol 10934. Springer, Cham, pp. 49‑63, DOI: 10.1007/978-3-319-96136-1_5.
  2. Bin, Luo & Bo, Liu & Yu, Zhu. (2020). “The Video Detection of Human Respiratory Motion Based on Sequential Images”, International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019, pp. 992‑997, DOI: 10.1007/978-3-030-25128-4_122.
  3. Firas Hamid (2020). “Study and Analysis Real-Time Methods of Tracking Objects on GPS and Mobile Telephone Towers Stations”, Proceedings of the 1st International Multi-Disciplinary Conference Theme: Sustainable Development and Smart Planning, IMDC-SDSP 2020, pp. 20‑29, DOI: 10.4108/eai.28-6-2020.2298219.
  4. Wang, Y., Idoughi, R., Heidrich, W. (2020). “Stereo Event-Based Particle Tracking Velocimetry for 3D Fluid Flow Reconstruction”, Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12374. Springer, Cham, pp. 36‑53, DOI: 10.1007/978-3-030-58526-6_3.
  5. Jung, K., Kim, Y., Lim, H., Myung, H. (2021). “ALVIO: Adaptive Line and Point Feature-Based Visual Inertial Odometry for Robust Localization in Indoor Environments”, RiTA 2020. Lecture Notes in Mechanical Engineering. Springer, Singapore, pp. 171‑184, DOI: 10.1007/978-981-16-4803-8_19.
  6. Hemalatha, R. et al., (2018). “Active Contour Based Segmentation Techniques for Medical Image Analysis”, in R. Koprowski (ed.), Medical and Biological Image Analysis, IntechOpen, London, pp. 17‑34, DOI: 10.5772/intechopen. 74576.
  7. Shaikh, S., Saeed, K., Chaki, N. (2014). “Moving Object Detection Using Background Subtraction”, SpringerBriefs in Computer Science. Springer, Cham, pp. 15‑23, DOI: 10.1007/978-3-319-07386-6.
  8. Fastiuk Y., Bachynskyy R., Huzynets N. (2021). “Methods of Vehicle Recognition and Detecting Traffic Rules Violations on Motion Picture Based on OpenCV Framework”, ACPS. 2021; Volume 6, Number 2, pp. 105‑111, DOI: 10.23939/acps2021.02.105.