Abstract
Personal customer care is one of the advantages of physical retail over its online competition, but cost pressure forces retailers to deploy staff as efficiently as possible resulting in a trend of staff reduction. For staff and managers it becomes harder to keep track of what is happening in a store. Situations that would benefit from intervention like cases of aimless customers, lost children or shoplifting go unnoticed. To this end, real-time tracking systems can provide managers with live data on the current in-store situation, but analysis methods are necessary to actually interpret these data. In particular, anomaly detection can highlight unusual situations that require a closer look. Unfortunately, existing algorithms are not well-suited for a retail scenario as they were designed for different use cases or are slow to compute. To resolve this, we investigate the use of long short-term memory autoencoders, which have recently shown to be successful in related scenarios, for real-time detection of unusual customer behavior. As we demonstrate, autoencoders reconcile the precision of reliable methods that have poor performance with a speed suitable for practical use.
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References
Chebiyyam, M., Reddy, R.D., Dogra, D.P., Bhaskar, H., Mihaylova, L.: Motion anomaly detection and trajectory analysis in visual surveillance. Multimedia Tools Appl. 77(13), 16223–16248 (2017). https://doi.org/10.1007/s11042-017-5196-6
Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Soft+ hardwired attention: an LSTM framework for human trajectory prediction and abnormal event detection. Neural Netw. 108, 466–478 (2018)
Forbes: Too few retail workers on the floor, too few retail sales and profits on p&l statement (2017). https://www.forbes.com/sites/pamdanziger/2017/12/16/too-few-retail-workers-on-the-floor-too-few-retail-sales-and-profits-on-pl-statement/
Gatt, T., Seychell, D., Dingli, A.: Detecting human abnormal behaviour through a video generated model. In: 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 264–270. IEEE (2019)
Guo, Y., Bardera, A.: SHNN-CAD+: an improvement on shnn-cad for adaptive online trajectory anomaly detection. Sensors 19(1), 84 (2019)
Hernandez, D.A.M., Nalbach, O., Werth, D.: How computer vision provides physical retail with a better view on customers. In: 2019 IEEE 21st Conference on Business Informatics (CBI), vol. 1, pp. 462–471. IEEE (2019)
Hinton, G.E., Zemel, R.S.: Autoencoders, minimum description length and Helmholtz free energy. In: Advances in Neural Information Processing Systems, pp. 3–10 (1994)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Keras: The python deep learning library. https://keras.io
Kieu, T., Yang, B., Guo, C., Jensen, C.S.: Outlier detection for time series with recurrent autoencoder ensembles. In: 28th International Joint Conference on Artificial Intelligence (2019)
Larson, J., Bradlow, E., Fader, P.: An exploratory look at supermarket shopping paths. Int. J. Res. Market. 22(4), 395–414 (2005)
Laxhammar, R., Falkman, G.: Online learning and sequential anomaly detection in trajectories. IEEE Trans. Pattern Anal. Mach. Intel. 36(6), 1158–1173 (2013)
Lemon, K.N., Verhoef, P.C.: Understanding customer experience throughout the customer journey. J. Market. 80(6), 69–96 (2016)
Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., Shroff, G.: LSTM-based encoder-decoder for multi-sensor anomaly detection (2016). arXiv preprint arXiv:1607.00148
NSW Justice: Shoplifting: Signs and prevention. http://www.crimeprevention.nsw.gov.au/Documents/RetailSecurityResource/04_Sh oplifting-signs_and_prevention.pdf
Owens, J., Hunter, A.: Application of the self-organising map to trajectory classification. In: Proceedings of 3rd IEE International Workshop on Visual Surveillance, pp. 77–83 (2000)
Quuppa: Quuppa intelligent locating system. https://quuppa.com
Sorensen, H., Bogomolova, S., Anderson, K., Trinh, G., Sharp, A., Kennedy, R., Page, B.: Fundamental patterns of in-store shopper behavior. J. Retail. Consum. Serv. 37, 182–194 (2017)
Technitis, G., Othman, W., Safi, K., Weibel, R.: From a to b, randomly: a point-to-point random trajectory generator for animal movement. Int. J. Geog. Inf. Sci. 29(6), 912–934 (2015)
Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: Proceedings 18th International Conference on Data Engineering, pp. 673–684. IEEE (2002)
Yada, K.: String analysis technique for shopping path in a supermarket. J. Intel. Inf. Syst. 36(3), 385–402 (2011)
Yan, P., Zeng, D.D.: Clustering customer shopping trips with network structure. In: ICIS 2008 Proceedings - 29th International Conference on Information Systems, p. 28 (2008)
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This work is based on VICAR, a project partly funded by the German ministry of education and research (BMBF), reference number 01IS17085C. The authors are responsible for the publication’s content.
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Nalbach, O., Bauer, S., Dahlem, N., Werth, D. (2020). Real-Time Detection of Unusual Customer Behavior in Retail Using LSTM Autoencoders. In: Abramowicz, W., Klein, G. (eds) Business Information Systems. BIS 2020. Lecture Notes in Business Information Processing, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-030-53337-3_7
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DOI: https://doi.org/10.1007/978-3-030-53337-3_7
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