SHOP : Store Habits Of People

Identifying a shopper’s in-store preferences, attitudes and intentions is an important objective for retailers. Some initial work has aimed to identify shoppers’ activities while visiting malls [2]. Current techniques for understanding a shopper’s behaviour and intent, such as shadowing and video analysis [1], do not scale well or suffer from privacy concerns. In this work we propose a system called SHOP, that utilizes smartphone-embedded sensors to capture and derive features from a user’s in-store movement and activity patterns, and then uses the feature vector to classify the intent of the shopper.