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Surveillance systems in shopping malls or supermarkets are usually designated for assuring safety and detecting abnormal behavior. We used the distributed video cameras system to design digital shopping assistants which assess the behavior of customers while shopping, detect when they need assistance, and offer their support in case there is a selling opportunity. In this paper we propose a system for analyzing human behavior patterns related to products interaction, which could reveal the customer's level of interest. We extracted discriminative features for basic action detection and analyzed different statistical and spatio-temporal classification methods, which capture relations between frames, features, and basic actions. Our experiments show that it is possible to accurately recognize different shopping related actions (85.7%) and discriminate between the proposed levels of interest in (88%) of the cases.