In several large retail stores, such as malls, sport or food stores, the customer often feels lost due to the difficulty in finding a product. Although these large stores usually have visual signs to guide customers towards specific products, sometimes these signs are also hard to find and are not updated. In this paper, we propose a system that jointly combines deep learning and augmented reality techniques to provide the customer with useful information. First, the proposed system learns the visual appearance of different areas in the store using a deep learning architecture. Then, customers can use their mobile devices to take a picture of the area where they are located within the store. Uploading this image to the system trained for image classification, we are able to identify the area where the customer is located. Then, using this information and novel augmented reality techniques, we provide information about the area where the customer is located: route to another area where a product is available, 3D product visualization, user location, analytics, etc. The system developed is able to successfully locate a user in an example store with 98% accuracy. The combination of deep learning systems together with augmented reality techniques shows promising results towards improving user experience in retail/commerce applications: branding, advance visualization, personalization, enhanced customer experience, etc.
Over one billion people in the world live with some form of disability. This is incessantly increasing due to aging population and chronic diseases. Among the emerging social needs, rehabilitation services are the most required. However, they are scarce and expensive what considerably limits access to them. In this paper, we propose EVA, an augmented reality platform to engage and supervise rehabilitation sessions at home using low-cost sensors. It also stores the user's statistics and allows therapists to tailor the exercise programs according to their performance. This system has been evaluated in both qualitative and quantitative ways obtaining very promising results.
As it is well known, some versions of the Pepper robot provide poor depth perception due to the lenses it has in front of the tridimensional sensor. In this paper, we present a method to improving that faulty 3D perception. Our proposal is based on a combination of the actual depth readings of Pepper and a deep learning-based monocular depth estimation. As shown, the combination of both of them provides a better 3D representation of the scene. In previous works we made an initial approximation of this fusion technique, but it had some drawbacks. In this paper we analyze the pros and cons of the Pepper readings, the monocular depth estimation method and our previous fusion method. Finally, we demonstrate that the proposed fusion method outperforms them all.
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