Challenges in Image-based Parking Lots Management
Abstract
With the increase in urban population, cities have a great need to use methods supported by Artificial Intelligence techniques to improve urban mobility in traffic increasingly congested by the growing number of vehicles in circulation. Studies show that traffic congestion is aggravated by up to 30% by drivers looking for parking spaces. This paper summarizes open issues in image-based parking space management and lists our research efforts combining machine learning techniques, edge computing, and image processing.
Keywords:
Parking lot, machine learning, computer vision
References
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Nurullayev, S. and Lee, S.-W. (2019). Generalized parking occupancy analysis based on dilated convolutional neural network. Sensors, 19(2):277.
Padmasiri, H., Madurawe, R., Abeysinghe, C., and Meedeniya, D. (2020). Automated vehicle parking occupancy detection in real-time. In MERCon, pages 1–6. IEEE.
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Ahrnbom, M., Astrom, K., and Nilsson, M. (2016). Fast classification of empty and occupied parking spaces using integral channel features. In Proceedings of the IEEE CVPR Workshops, volume 2016, pages 1609–1615. IEEE.
Ahvenniemi, H., Huovila, A., Pinto-Seppä, I., and Airaksinen, M. (2017). What are the differences between sustainable and smart cities? Cities, 60:234–245.
Almeida, P. R., Oliveira, L. S., Britto, A. S., and Sabourin, R. (2018). Adapting dynamic classifier selection for concept drift. ESWA, 104:67–85.
Amato, G., Carrara, F., Falchi, F., Gennaro, C., Meghini, C., and Vairo, C. (2017). Deep learning for decentralized parking lot occupancy detection. ESWA, 72:327–334.
Bohush, R., Yarashevich, P., Ablameyko, S., and Kalganova, T. (2018). Extraction of image parking spaces in intelligent video surveillance systems. MGV, 27(1-4):47–62.
Chen, L.-C., Sheu, R.-K., Peng, W.-Y., Wu, J.-H., and Tseng, C.-H. (2020). Video-based parking occupancy detection for smart control system. Applied Sciences, 10(3):1079.
de Almeida, P. R., Oliveira, L. S., Britto, A. S., Silva, E. J., and Koerich, A. L. (2015). Pklot – a robust dataset for parking lot classification. ESWA, 42(11):4937 – 4949.
DESA, U. N. (2019). World urbanization prospects: The 2018 revision. United Nations: New York, NY, USA.
Djahel, S., Salehie, M., Tal, I., and Jamshidi, P. (2013). Adaptive traffic management for secure and efficient emergency services in smart cities. In 2013 IEEE PERCOM Workshops, pages 340–343. IEEE.
Hurst-Tarrab, N., Chang, L., Gonzalez-Mendoza, M., and Hernandez-Gress, N. (2020). Robust parking block segmentation from a surveillance camera perspective. Applied Sciences, 10(15):5364.
Jensen, T. H., Schmidt, H. T., Bodin, N. D., Nasrollahi, K., and Moeslund, T. B. (2017). Parking space occupancy verification-improving robustness using a convolutional neural network. In VISAPP, volume 6, pages 311–318. SCITEPRESS.
Koumetio Tekouabou, S. C., Abdellaoui Alaoui, E. A., Cherif, W., and Silkan, H. (2020). Improving parking availability prediction in smart cities with iot and ensemble-based model. Journal of King Saud University - Computer and Information Sciences.
Letaifa, S. B. (2015). How to strategize smart cities: Revealing the smart model. Journal of business research, 68(7):1414–1419.
Li, Z., Shahidehpour, M., Bahramirad, S., and Khodaei, A. (2017). Optimizing traffic signal settings in smart cities. IEEE Transactions on Smart Grid, 8(5):2382–2393.
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L. (2014). Microsoft coco: Common objects in context. In ECCV, pages 740–755. Springer.
Nagy, A. M. and Simon, V. (2018). Survey on traffic prediction in smart cities. Pervasive and Mobile Computing, 50:148–163.
Nieto, R. M., Garcı́a-Martı́n, Á., Hauptmann, A. G., and Martı́nez, J. M. (2018). Automatic vacant parking places management system using multicamera vehicle detection. IEEE ITS, 20(3):1069–1080.
Nurullayev, S. and Lee, S.-W. (2019). Generalized parking occupancy analysis based on dilated convolutional neural network. Sensors, 19(2):277.
Padmasiri, H., Madurawe, R., Abeysinghe, C., and Meedeniya, D. (2020). Automated vehicle parking occupancy detection in real-time. In MERCon, pages 1–6. IEEE.
Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1):30–39.
Varghese, A. and Sreelekha, G. (2019). An efficient algorithm for detection of vacant spaces in delimited and non-delimited parking lots. IEEE ITS.
Vı́tek, S. and Melničuk, P. (2018). A distributed wireless camera system for the management of parking spaces. Sensors, 18(1):69.
Zhang, C. and Du, B. (2020). Image-based approach for parking-spot detection with occlusion handling. JTE, Part A: Systems, 146(9):04020098.
Zhang, W., Yan, J., and Yu, C. (2019). Smart parking system based on convolutional neural network models. In ICISCE, pages 561–566. IEEE, IEEE.
Published
2021-07-18
How to Cite
ALMEIDA, Paulo R. L. de; ALMEIDA, Eduardo C. de.
Challenges in Image-based Parking Lots Management. In: INTEGRATED SOFTWARE AND HARDWARE SEMINAR (SEMISH), 48. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 106-113.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2021.15812.
