References
- L. BAO, Q. WANG, and Y. JIANG, “Review of Digital twin for intelligent transportation system,” in 2021 International Conference on Information Control, Electrical Engineering and Rail Transit (ICEERT), 2021, pp. 309–315.
- S. M. A. SHEIK., N. JOSHI, B. GEORGE, and L. VANAJAKSHI, “Application of random forest algorithm to classify vehicles detected by a multiple inductive loop system,” in 2012 15th International IEEE Conference on Intelligent Transportation Systems, 2012, pp. 491–495.
- L. MAINETTI, L. PALANO, L. PATRONO, M. L. STEFANIZZI, and R. VERGALLO, “Integration of RFID and WSN technologies in a Smart Parking System,” in 2014 22nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2014, pp. 104–110.
- K. MICKO, P. PAPCUN, and I. ZOLOTOVA, “Review of IoT sensor systems used for monitoring the road infrastructure,” Sensors, vol. 23, no. 9, p. 4469, 2023.
- S. JUNG, U. LEE, J. JUNG, and D. H. SHIM, “Real-time Traffic Sign Recognition system with deep convolutional neural network,” in 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), 2016, pp. 31–34.
- A. JAIN and K. SHAMBAVI, “Energy Efficient Smart Parking System Using Peizoelectric Material And Sensors.”
- S. VITEK and P. MELNICUK, “A Distributed Wireless Camera System for the Management of Parking Spaces,” Sensors, vol. 18, no. 1, 2018. [Online]. Available: https://www.mdpi.com/1424-8220/18/1/69
- W. A. JABBAR, C. W. WEI, N. A. A. M. AZMI, and N. A. HAIRONNAZLI, “An IoT Raspberry Pi-based parking management system for smart campus,” INTERNET OF THINGS, vol. 14, JUN 2021.
- M. AWALUDIN and V. YASIN, “Application Of Oriented Fast And Rotated Brief (Orb) And Bruteforce Hamming In Library Opencv For Classification Of Plants,” JISAMAR (Journal of Information System, Applied, Management, Accounting and Research), vol. 4, no. 3, pp. 51–59, 2020.
- K. MICKO, F. BABIC, P. PAPCUN, and I. ZOLOTOVA, “Temporary parking via computer vision and deep learning,” in 2022 IEEE 20th Jubilee World Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2022, pp. 000 455–000 460.
- Z. SHANGGUAN, L. WANG, J. ZHANG, and W. DONG, “Vision-Based Object Recognition and Precise Localization for Space Body Control,” International Journal of Aerospace Engineering, vol. 2019, p. 7050915, Mar 2019. [Online]. Available: https://doi.org/10.1155/2019/7050915
- R. SZELISKI, Computer vision: algorithms and applications. Springer Nature, 2022.
- K. MICKO and F. BABIC, “Evidencia dočasného parkovania pomocou metód počítačového videnia a umelej inteligencie,” Electrical Engineering and Informatics XII, pp. 61–64, 2021.
- L. P. COELHO, “Mahotas: Open source software for scriptable computer vision,” arXiv preprint arXiv:1211.4907, 2012.
- B. TUOMANEN, Hands-On GPU Programming with Python and CUDA: Explore high-performance parallel computing with CUDA. Packt Publishing Ltd, 2018.
- N. IDRIS, C. F. M. FOOZY, and P. SHAMALA, “A generic review of web technology: Django and flask,” International Journal of Advanced Science Computing and Engineering, vol. 2, no. 1, pp. 34–40, 2020.
- F. GESSERT, W. WINGERATH, S. FRIEDRICH, and N. RITTER, “NoSQL database systems: a survey and decision guidance,” Computer Science-Research and Development, vol. 32, pp. 353–365, 2017.
- G. D. SAMARAWEERA and J. M. CHANG, “Security and Privacy Implications on Database Systems in Big Data Era: A Survey,” IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 1, pp. 239–258, 2021.
- A. BOICEA, F. RADULESCU, and L. I. AGAPIN, “MongoDB vs Oracle – Database Comparison,” in 2012 Third International Conference on Emerging Intelligent Data and Web Technologies, 2012, pp. 330–335.
- B. JOSHI and B. JOSHI, “Event Handling,” Beginning jQuery 2 for ASP. NET Developers: Using jQuery 2 with ASP. NET Web Forms and ASP. NET MVC, pp. 103–133, 2013.
- B. TOM, “Busy Parking Lot - Aerial Time-Lapse,” accessed: 26/4/2017. [Online]. Available: https://www.youtube.com/watch?v=yojapmOkIfg
- K. NOOR, “how to yolov5 counting vehicles in parking area yolov5 vehicles detection computer vision,” accessed: 11/2/2023. [Online]. Available: https://www.youtube.com/watch?v=9Wf7_2Bv5No
- M. GREGOR, R. PIRNIK, and D. NEMEC, “Transfer Learning for Classification of Parking Spots using Residual Networks,” Transportation Research Procedia, vol. 40, pp. 1327–1334, 2019, tRANSCOM 2019 13th International Scientific Conference on Sustainable, Modern and Safe Transport. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2352146519303527
- A. IGNATOV, R. TIMOFTE, and et al., “PIRM Challenge on Perceptual Image Enhancement on Smart-phones: Report,” in Computer Vision – ECCV 2018 Workshops, vol. 5, no. 15, Munich, Germany, September 2018, pp. 315–333.
- B. DAS, J. P. EBENEZER, and S. MUKHOPADHYAY, “A comparative study of single image fog removal methods,” The Visual Computer, vol. 38, no. 1, pp. 179–195, Jan 2022.
- B. CHENG, J. LI, Y. CHEN, and T. ZENG, “Snow Mask Guided Adaptive Residual Network for Image Snow Removal,” Computer Vision and Image Understanding, vol. 236, p. 103819, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1077314223001996
- X. FU, J. HUANG, X. DING, Y. LIAO, and J. PAISLEY, “Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal,” IEEE Transactions on Image Processing, vol. 26, no. 6, pp. 2944–2956, 2017.
- M.-C. LU, “Advancement of Chip Stacking Architectures and Interconnect Technologies for Image Sensors,” Journal of Electronic Packaging, vol. 144, no. 2, p. 020801, 09 2021.
- A. S. PARIHAR and A. JAVA, “Densely connected convolutional transformer for single image dehazing,” Journal of Visual Communication and Image Representation, vol. 90, p. 103722, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1047320322002425
- G. SAHU, A. SEAL, D. BHATTACHARJEE, M. NASIPURI, P. BRIDA, and O. KREJCAR, “Trends and Prospects of Techniques for Haze Removal From Degraded Images: A Survey,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 4, pp. 762–782, 2022.