Desafios na Gerência de Estacionamentos por Imagem
Resumo
Com o aumento na população urbana, as cidades tem grande necessidade de utilizar métodos apoiados por técnicas de Inteligência Artificial para melhorar a mobilidade urbana em trânsitos cada vez mais congestionados pelo crescente número de veículos em circulação. Estudos demonstram que o problema de congestionamento de trânsito é agravado em até 30% por veículos procurando vagas de estacionamento. Neste artigo sumarizamos problemas em aberto na gerência de vagas de estacionamento por imagem e listamos nossos esforços de pesquisa combinando técnicas de aprendizado de máquina, edge computing e processamento de imagens.
Palavras-chave:
Estacionamento, aprendizado de máquina, visão computacional
Referências
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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.
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.
Publicado
18/07/2021
Como Citar
ALMEIDA, Paulo R. L. de; ALMEIDA, Eduardo C. de.
Desafios na Gerência de Estacionamentos por Imagem. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (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.
