Abstract
Nowadays, Smart Cities applications are becoming steadily popular, thanks to their main objective of improving people daily habits. The services provided by the aforementioned applications may be either addressed to the entire digital population or narrowed towards a specific kind of audience, like drivers and pedestrians. In this sense, the proposed paper describes a Deep Learning solution designed to manage traffic control tasks in Smart Cities. It involves a network of smart lampposts, in charge of directly monitoring the traffic by means of a bullet camera, and equipped with an advanced System-on-Module where the data are efficiently processed. In particular, our solution provides both: i) a risk estimation module, and ii) a license plate recognition module. The first module analyses the scene by means of a Faster R-CNN, trained over an ad-hoc set of synthetically videos, to estimate the risk of potential traffic anomalies. Concurrently, the license plate recognition module, by leveraging on YOLO and Tesseract, is active for retrieving the plate number of the vehicles involved. Preliminary experimental findings, from a prototype of the solution applied in a real-world scenario, are provided.
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References
Ahad, M.A., Paiva, S., Tripathi, G., Feroz, N.: Enabling technologies and sustainable smart cities. Sustain. Cities Soc. 61, 102301 (2020)
Al-Heety, A.T., et al.: Moving vehicle detection from video sequences for traffic surveillance system. ITEGAM-JETIA 7(27), 41–48 (2021)
Al-Turjman, F., Lemayian, J.P.: Intelligence, security, and vehicular sensor networks in internet of things (IoT)-enabled smart-cities: an overview. Comput. Electr. Eng. 87, 106776 (2020)
Albatish, I.M., Abu-Naser, S.S.: Modeling and controlling smart traffic light system using a rule based system. In: 2019 International Conference on Promising Electronic Technologies (ICPET), pp. 55–60 (2019). https://doi.org/10.1109/ICPET.2019.00018
Appathurai, A., Sundarasekar, R., Raja, C., Alex, E.J., Palagan, C.A., Nithya, A.: An efficient optimal neural network-based moving vehicle detection in traffic video surveillance system. Circ. Syst. Signal Process. 39(2), 734–756 (2020)
Atzori, A., Barra, S., Carta, S., Fenu, G., Podda, A.S.: Heimdall: an AI-based infrastructure for traffic monitoring and anomalies detection. In: 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), pp. 154–159. IEEE (2021)
Barra, S., Bisogni, C., De Marsico, M., Ricciardi, S.: Visual question answering: which investigated applications? arXiv preprint arXiv:2103.02937 (2021)
Barra, S., Carta, S.M., Giuliani, A., Pisu, A., Podda, A.S., et al.: FootApp: an AI-powered system for football match annotation. arXiv preprint arXiv:2103.02938 (2021)
Barra, S., De Marsico, M., Cantoni, V., Riccio, D.: Using mutual information for multi-anchor tracking of human beings. In: Cantoni, V., Dimov, D., Tistarelli, M. (eds.) Biometric Authentication, pp. 28–39. Springer International Publishing, Cham (2014). https://doi.org/10.1007/978-3-319-13386-7_3
Bock, F., Di Martino, S., Origlia, A.: Smart parking: using a crowd of taxis to sense on-street parking space availability. IEEE Trans. Intell. Transp. Syst. 21(2), 496–508 (2020). https://doi.org/10.1109/TITS.2019.2899149
Bock, F., Di Martino, S.: On-street parking availaibilty data in San Francisco, from stationary sensors and high-mileage probe vehicles. Data Brief 25, 104039 (2019)
Braun, T., Fung, B.C., Iqbal, F., Shah, B.: Security and privacy challenges in smart cities. Sustain. Cities Soc. 39, 499–507 (2018)
Carta, S., Podda, A.S., Recupero, D.R., Saia, R.: A local feature engineering strategy to improve network anomaly detection. Future Internet 12(10), 177 (2020)
Chakraborty, M., Pramanick, A., Dhavale, S.V.: MobiSamadhaan—intelligent vision-based smart city solution. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds.) International Conference on Innovative Computing and Communications. AISC, vol. 1165, pp. 329–345. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-5113-0_24
Cho, Y., Jeong, H., Choi, A., Sung, M.: Design of a connected security lighting system for pedestrian safety in smart cities. Sustainability 11(5) (2019). https://doi.org/10.3390/su11051308, https://www.mdpi.com/2071-1050/11/5/1308
Choi, S., Kim, J.T., Choo, J.: Cars can’t fly up in the sky: improving urban-scene segmentation via height-driven attention networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020
Combs, T.S., Sandt, L.S., Clamann, M.P., McDonald, N.C.: Automated vehicles and pedestrian safety: Exploring the promise and limits of pedestrian detection. Am. J. Prev. Med. 56(1), 1–7 (2019)
Deng, J., Li, L., Zhang, B., Wang, S., Zha, Z., Huang, Q.: Syntax-guided hierarchical attention network for video captioning. IEEE Trans. Circ. Syst. Video Technol. (2021, in press)
Dhingra, S., Madda, R.B., Patan, R., Jiao, P., Barri, K., Alavi, A.H.: Internet of things-based fog and cloud computing technology for smart traffic monitoring. Internet Things, p. 100175 (2020). https://doi.org/10.1016/j.iot.2020.100175, https://www.sciencedirect.com/science/article/pii/S2542660519302100
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016
Sri Jamiya, S., Esther Rani, P.: An efficient method for moving vehicle detection in real-time video surveillance. In: Suresh, P., Saravanakumar, U., Hussein Al Salameh, M.S. (eds.) Advances in Smart System Technologies. AISC, vol. 1163, pp. 577–585. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-5029-4_47
Khan, L.U., Yaqoob, I., Tran, N.H., Kazmi, S.M.A., Dang, T.N., Hong, C.S.: Edge-computing-enabled smart cities: a comprehensive survey. IEEE Internet Things J. 7(10), 10200–10232 (2020). https://doi.org/10.1109/JIOT.2020.2987070
Khan, M.A., et al.: Human action recognition using fusion of multiview and deep features: an application to video surveillance. Multimedia Tools Appl. 1–27 (2020)
Malik, K.: Fast vehicle detection with probabilistic feature grouping and its application to vehicle tracking. In: Proceedings Ninth IEEE International Conference on Computer Vision, vol. 1, pp. 524–531 (2003). https://doi.org/10.1109/ICCV.2003.1238392
Li, W., Guo, H., Nejad, M., Shen, C.C.: Privacy-preserving traffic management: a blockchain and zero-knowledge proof inspired approach. IEEE Access 8, 181733–181743 (2020)
Li, Y., et al.: Multi-granularity tracking with modularlized components for unsupervised vehicles anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2020
Longo, R., Podda, A.S., Saia, R.: Analysis of a consensus protocol for extending consistent subchains on the bitcoin blockchain. Computation 8(3), 67 (2020)
Makhmutova, A., Anikin, I., Minnikhanov, R., Bolshakov, T., Dagaeva, M.: Detection of traffic anomalies for a safety system of smart city. In: Information Technology and Nanotechnology (ITNT-2020), pp. 638–645 (2020)
Michalopoulos, P.G.: Vehicle detection video through image processing: the autoscope system. IEEE Trans. Veh. Technol. 40(1), 21–29 (1991). https://doi.org/10.1109/25.69968
Neves, J.C., Moreno, J.C., Barra, S., Proença, H.: Acquiring high-resolution face images in outdoor environments: a master-slave calibration algorithm. In: 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1–8 (2015). https://doi.org/10.1109/BTAS.2015.7358744
Nguyen, K.T., Dinh, D.T., Do, M.N., Tran, M.T.: Anomaly detection in traffic surveillance videos with GAN-based future frame prediction. In: Proceedings of the 2020 International Conference on Multimedia Retrieval, pp. 457–463 (2020)
Otsu, N.: A threshold selection method from Gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Pagliara, F., Mauriello, F., Di Martino, S.: An analysis of the link between high speed transport and tourists’ behaviour. Tourism Int. Interdisc. J. 67(2), 116–125 (2019)
Pang, G., Shen, C., Cao, L., Hengel, A.V.D.: Deep learning for anomaly detection: a review. ACM Comput. Surveys (CSUR) 54(2), 1–38 (2021)
Piccinelli, L.: Raddrizzare il contenuto di un’immagine, November 2016. https://luca-picci.medium.com/raddrizzare-il-contenuto-di-unimmagine-37f9bbc16207
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016
Sreenu, G., Durai, M.S.: Intelligent video surveillance: a review through deep learning techniques for crowd analysis. J. Big Data 6(1), 1–27 (2019)
Suzuki, L.R.: Smart cities IoT: enablers and technology road map. In: Rassia, S.T., Pardalos, P.M. (eds.) Smart City Networks. SOIA, vol. 125, pp. 167–190. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61313-0_10
Yang, Y.T., Chou, L.D., Tseng, C.W., Tseng, F.H., Liu, C.C.: Blockchain-based traffic event validation and trust verification for VANETs. IEEE Access 7, 30868–30877 (2019)
Fu, Z., Hu, W., Tan, T.: Similarity based vehicle trajectory clustering and anomaly detection. In: IEEE International Conference on Image Processing 2005, vol. 2, pp. II-602 (2005). https://doi.org/10.1109/ICIP.2005.1530127
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Balia, R., Barra, S., Carta, S., Fenu, G., Podda, A.S., Sansoni, N. (2021). A Deep Learning Solution for Integrated Traffic Control Through Automatic License Plate Recognition. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12951. Springer, Cham. https://doi.org/10.1007/978-3-030-86970-0_16
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