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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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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