Abstract
The problem of transport is one of the fundamental problems in today's large cities and one of the fundamental pillars on which work is being done under the paradigm of smart cities. Although the use of public transport is encouraged in cities, there are always problems related to private transport parking availability. This work focuses on the development of an algorithm for monitoring parking spaces in open-air car parks, which is effective and simple from the point of view of the costs required for its deployment and robust in detecting the state of the car park. To this end, an algorithm based on Deep Learning has been developed for processing the status images of different car parks, obtaining real-time information on the real status of the car park, without the need for extensive deployment of devices in the car park. The proposed solution has been shown to outperform other image-based solutions for the same problem.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Santana, E.F.Z., Chaves, A.P., Gerosa, M.A., Kon, F., Milojicic, D.S.: Software platforms for smart cities. ACM Comput. Surv. 50, 1–37 (2018). https://doi.org/10.1145/3124391
Monzon, A.: Smart cities concept and challenges: bases for the assessment of smart city projects. In: Helfert, M., Krempels, K.-H., Klein, C., Donellan, B., Guiskhin, O. (eds.) Smart Cities, Green Technologies, and Intelligent Transport Systems: 4th International Conference, SMARTGREENS 2015, and 1st International Conference VEHITS 2015, Lisbon, Portugal, May 20-22, 2015, Revised Selected Papers, pp. 17–31. Springer International Publishing, Cham (2015). https://doi.org/10.1007/978-3-319-27753-0_2
Khatoun, R., Zeadally, S.: Smart cities: concepts, architectures, research opportunities. Commun. ACM. 59, 46–57 (2016). https://doi.org/10.1145/2858789
Alsafery, W., Alturki, B., Reiff-Marganiec, S., Jambi, K.: Smart car parking system solution for the internet of things in smart cities. In: 1st International Conference on Computer Application and Information Security ICCAIS 2018 (2018). https://doi.org/10.1109/CAIS.2018.8442004
Melnyk, P., Djahel, S., Nait-Abdesselam, F.: Towards a smart parking management system for smart cities. In: 5th IEEE International Smart Cities Conference, ISC2 2019, pp. 542–546 (2019). https://doi.org/10.1109/ISC246665.2019.9071740
Bagula, A., Castelli, L., Zennaro, M.: On the design of smart parking networks in the smart cities: an optimal sensor placement model. Sensors (Switzerland). 15, 15443–15467 (2015). https://doi.org/10.3390/s150715443
Alam, M., et al.: Real-time smart parking systems integration in distributed ITS for smart cities. J. Adv. Transp. 2018, 1–13 (2018). https://doi.org/10.1155/2018/1485652
Ji, Z., Ganchev, I., O’Droma, M., Zhao, L., Zhang, X.: A cloud-based car parking middleware for IoT-based smart cities: design and implementation. Sensors (Switzerland). 14, 22372–22393 (2014). https://doi.org/10.3390/s141222372
Rizvi, S.R., Zehra, S., Olariu, S.: ASPIRE: an agent-oriented smart parking recommendation system for smart cities. IEEE Intell. Transp. Syst. Mag. 11, 48–61 (2019). https://doi.org/10.1109/MITS.2018.2876569
Koumetio Tekouabou, S.C., Abdellaoui Alaoui, E.A., Cherif, W., Silkan, H.: Improving parking availability prediction in smart cities with IoT and ensemble-based model. J. King Saud Univ. - Comput. Inf. Sci. 1–11 (2020). https://doi.org/10.1016/j.jksuci.2020.01.008
Kumar Gandhi, B.M., Kameswara Rao, M.: A prototype for IoT based car parking management system for smart cities. Indian. J. Sci. Technol. 9, 17 (2016). https://doi.org/10.17485/ijst/2016/v9i17/92973
Wang, T., Yao, Y., Chen, Y., Zhang, M., Tao, F., Snoussi, H.: Auto-sorting system toward smart factory based on deep learning for image segmentation. IEEE Sens. J. 18, 8493–8501 (2018). https://doi.org/10.1109/JSEN.2018.2866943
Khanna, A., Anand, R.: IoT based smart parking system. In: 2016 International Conference on Internet Things Applications, IOTA 2016, pp. 266–270 (2016). https://doi.org/10.1109/IOTA.2016.7562735
Grodi, R., Rawat, D.B., Rios-Gutierrez, F.: Smart parking: parking occupancy monitoring and visualization system for smart cities. In: Conference Proceedings of - IEEE SOUTHEASTCON, 2016-July, pp. 1–5 (2016). https://doi.org/10.1109/SECON.2016.7506721
Sadhukhan, P.: An IoT-based E-parking system for smart cities. 2017 Int. Conf. Adv. Comput. Commun. Informatics. 1062–1066 (2017). https://doi.org/10.1109/ICACCI.2017.8125982.
Baroffio, L., Bondi, L., Cesana, M., Redondi, A.E., Tagliasacchi, M.: A visual sensor network for parking lot occupancy detection in smart cities. In: Proceedings of IEEE World Forum Internet Things, WF-IoT 2015, pp. 745–750 (2015). https://doi.org/10.1109/WF-IoT.2015.7389147
Zacepins, A., Komasilovs, V., Kviesis, A.: Implementation of smart parking solution by image analysis. In: VEHITS 2018 – Proceedings of 4th Vehicle Technology and Intelligent Transport Systems, 2018-March, pp. 666–669 (2018). https://doi.org/10.5220/0006629706660669
Tatulea, P., Calin, F., Brad, R., Brâncovean, L., Greavu, M.: An image feature-based method for parking lot occupancy. Futur. Internet. 11, 169 (2019). https://doi.org/10.3390/fi11080169
Tschentscher, M., Koch, C., König, M., Salmen, J., Schlipsing, M.: Scalable real-time parking lot classification: An evaluation of image features and supervised learning algorithms. Proceedings of International Joint Conference on Neural Networks, 2015-September (2015). https://doi.org/10.1109/IJCNN.2015.7280319.
Lopez, M., Griffin, T., Ellis, K., Enem, A., Duhan, C.: Parking lot occupancy tracking through image processing. In: Proceedings of 34th International Conference on Computers and Their Applications CATA 2019, vol. 58, pp. 265–270 (2019). https://doi.org/10.29007/69m7
Paidi, V., Fleyeh, H.: Parking occupancy detection using thermal camera. In: VEHITS 2019 – Proceedings of 5th International Conference on Vehicle Technology and Intelligent Transport System, pp. 483–490 (2019). https://doi.org/10.5220/0007726804830490.
Farag, M.S., Mohie El Din, M.M., El Shenbary, H.A.: Deep learning versus traditional methods for parking lots occupancy classification. Indones. J. Electr. Eng. Comput. Sci. 19, 964–973 (2020). https://doi.org/10.11591/ijeecs.v19i2.pp964-973
Amato, G., Carrara, F., Falchi, F., Gennaro, C., Meghini, C., Vairo, C.: Deep learning for decentralized parking lot occupancy detection. Expert Syst. Appl. 72, 327–334 (2017). https://doi.org/10.1016/j.eswa.2016.10.055
Acharya, D., Yan, W., Khoshelham, K.: Real-time image-based parking occupancy detection using deep learning. CEUR Workshop Proc. 2087, 33–40 (2018)
Chen, L.C., Sheu, R.K., Peng, W.Y., Wu, J.H., Tseng, C.H.: Video-based parking occupancy detection for smart control system. Appl. Sci. 10, 1079 (2020). https://doi.org/10.3390/app10031079
De Almeida, P.R.L., Oliveira, L.S., Britto, A.S., Silva, E.J., Koerich, A.L.: PKLot-a robust dataset for parking lot classification. Expert Syst. Appl. 42, 4937–4949 (2015). https://doi.org/10.1016/j.eswa.2015.02.009
GitHub-jekhor/aerial-cars-dataset: Dataset for car detection on aerial photos applications. https://github.com/jekhor/aerial-cars-dataset. Accessed 07 Apr 2021
Redmon, J., Farhadi, A.: YOLOv3: An incremental improvement (2018). arXiv
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Iglesias, A.T., Pastor-López, I., Urquijo, B.S., García-Bringas, P. (2021). Hybrid Deep Learning Approach for Efficient Outdoor Parking Monitoring in Smart Cities. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2021. Lecture Notes in Computer Science(), vol 12886. Springer, Cham. https://doi.org/10.1007/978-3-030-86271-8_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-86271-8_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86270-1
Online ISBN: 978-3-030-86271-8
eBook Packages: Computer ScienceComputer Science (R0)