Abstract
Mainly in the last years the analysis of the behaviour of shoppers inside a store is becoming a very attracting issue. Thanks to the use of technologies and artificial intelligence based approaches novel methods are applied to automatically evaluate the movement of shopper in the store, the interactions with the products on the shelves, the time spent inside the store, and more, in a passive mode without interviewing the consumers and preserving their privacy. The aim of this paper is to propose a method based on a Support Vector Machine classifier that classifies the interactions of the shopper with products solving the problem of unclassifiable interactions, when carts, baskets, or other objects are temporarily placed in front of the shelves. The implemented system is also able to solve the overcrowding problem that emerges when several shoppers are close to each other, and entering the analysis area of the camera together, were detected as a single person and not as distinct persons.
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References
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Dan, B., Kim, Y., Suryanto, Jung, J., Ko, S.: Robust people counting system based on sensor fusion. IEEE Trans. Consum. Electron. 58(3), 1013–1021 (August 2012). https://doi.org/10.1109/TCE.2012.6311350
Ferracuti, N., Norscini, C., Frontoni, E., Gabellini, P., Paolanti, M., Placidi, V.: A business application of RTLS technology in intelligent retail environment: Defining the shopper’s preferred path and its segmentation. J. Retail. Consum. Serv. 47, 184–194 (2019)
Hahsler, M., Hornik, K.: TSP-infrastructure for the traveling salesperson problem. J. Stat. Softw. 23(2), 1–21 (2007)
Larson, J.S., Bradlow, E.T., Fader, P.S.: An exploratory look at supermarket shopping paths. Int. J. Res. Mark. 22(4), 395–414 (2005)
Li, H.B., Wang, W., Ding, H.W., Dong, J.: Mining paths and transactions data to improve allocating commodity shelves in supermarket. In: Proceedings of 2012 IEEE International Conference on Service Operations and Logistics, and Informatics, pp. 102–106. IEEE (2012)
Liciotti, D., Paolanti, M., Frontoni, E., Zingaretti, P.: People detection and tracking from an RGB-D camera in top-view configuration: review of challenges and applications. In: Battiato, S., Farinella, G.M., Leo, M., Gallo, G. (eds.) ICIAP 2017. LNCS, vol. 10590, pp. 207–218. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70742-6_20
Liciotti, D., Paolanti, M., Pietrini, R., Frontoni, E., Zingaretti, P.: Convolutional networks for semantic heads segmentation using top-view depth data in crowded environment. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 1384–1389. IEEE (2018)
Liu, J., Liu, Y., Zhang, G., Zhu, P., Chen, Y.Q.: Detecting and tracking people in real time with RGB-D camera. Pattern Recogn. Lett. 53, 16–23 (2015)
Martini, M., Paolanti, M., Frontoni, E.: Open-world person re-identification with RGBD camera in top-view configuration for retail applications. IEEE Access 8, 67756–67765 (2020)
Melià-Seguí, J., Pous, R.: Human-object interaction reasoning using RFID-enabled smart shelf. In: 2014 International Conference on the Internet of Things (IOT), pp. 37–42. IEEE (2014)
Moiseeva, A., Timmermans, H.: Imputing relevant information from multi-day GPS tracers for retail planning and management using data fusion and context-sensitive learning. J. Retail. Consum. Serv. 17(3), 189–199 (2010)
Paolanti, M., Frontoni, E.: Multidisciplinary pattern recognition applications: a review. Comput. Sci. Rev. 37, 100276 (2020)
Paolanti, M., Liciotti, D., Pietrini, R., Mancini, A., Frontoni, E.: Modelling and forecasting customer navigation in intelligent retail environments. J. Intell. Robot. Syst. 91(2), 165–180 (2018)
Paolanti, M., Pietrini, R., Mancini, A., Frontoni, E., Zingaretti, P.: Deep understanding of shopper behaviours and interactions using RGB-D vision. Mach. Vis. Appl. 31(7), 1–21 (2020)
Paolanti, M., et al.: Person re-identification with RGB-D camera in top-view configuration through multiple nearest neighbor classifiers and neighborhood component features selection. Sensors 18(10), 3471 (2018)
Ravnik, R., Solina, F., Zabkar, V.: Modelling in-store consumer behaviour using machine learning and digital signage audience measurement data. In: Distante, C., Battiato, S., Cavallaro, A. (eds.) VAAM 2014. LNCS, vol. 8811, pp. 123–133. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12811-5_9
Sorensen, H.: The science of shopping. Mark. Res. 15(3), 30–30 (2003)
Sorensen, H., et al.: Fundamental patterns of in-store shopper behavior. J. Retail. Consum. Serv. 37, 182–194 (2017)
Sturari, M., et al.: Robust and affordable retail customer profiling by vision and radio beacon sensor fusion. Pattern Recogn. Lett. 81, 30–40 (2016)
Zhao, Y., Zhang, Y., Cheng, R., Wei, D., Li, G.: An enhanced histogram of oriented gradients for pedestrian detection. IEEE Intell. Transp. Syst. Mag. 7(3), 29–38 (2015)
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Della Sciucca, L., Manco, D., Contigiani, M., Pietrini, R., Di Bello, L., Placidi, V. (2021). Shoppers Detection Analysis in an Intelligent Retail Environment. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12662. Springer, Cham. https://doi.org/10.1007/978-3-030-68790-8_42
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