Person re-identification is an important topic in retail, scene monitoring, human-computer interaction, people counting, ambient assisted living and many other application fields. A dataset for person re-identification TVPR (Top View Person Re-Identification) based on a number of significant features derived from both depth and color images has been previously built. This dataset uses an RGB-D camera in a top-view configuration to extract anthropometric features for the recognition of people in view of the camera, reducing the problem of occlusions while being privacy preserving. In this paper, we introduce a machine learning method for person re-identification using the TVPR dataset. In particular, we propose the combination of multiple k-nearest neighbor classifiers based on different distance functions and feature subsets derived from depth and color images. Moreover, the neighborhood component feature selection is used to learn the depth features’ weighting vector by minimizing the leave-one-out regularized training error. The classification process is performed by selecting the first passage under the camera for training and using the others as the testing set. Experimental results show that the proposed methodology outperforms standard supervised classifiers widely used for the re-identification task. This improvement encourages the application of this approach in the retail context in order to improve retail analytics, customer service and shopping space management.

In this study, we present a medical imaging method to estimate the preterm infant respiratory rate with a non invasive embedded RGB-D sensor. The respiratory rate is derived by measuring morphological chest wall movements with a depth sensor. The performances of our method are evaluated by comparing the values of respiratory rate measurements obtained using our method with those resulting from a standard reference device, used as a benchmark. Experimental results showed that the proposed method can correctly measure the respiratory rate in preterm infants and activates an alarm signal when respiratory rate values go out of the physiological range or when the infant remains stationary for a long time. As future development, the project can be used as multi-purpose non invasive test on movements and their symmetry or on the infants’ ability to correctly inflate.

The success of pervasive smart environments lies in the capacity to involve visitors to interact with them. It is essential for retail stores. In this paper we describe the setting-up of a low cost system for the indoor localization and customer interaction, developed with a complex infrastructure of wireless embedded sensors. The creation of a responsive store allows customers to connect the real world to their smart devices and will overcome the lack of ubiquity in public spaces; furthermore, from in-venue analytics and proximity sensor it is possible to customize the user experience. First of all we describe the whole sensor network. We go in deep into the active beacon technology adopted for this study. Then, thanks to the analytics, we present a data evaluation with the aim of determining the best sensor arrangement, according to several user tests. Beside the strong enhancement of human interaction, the results of our essay demonstrate how embedded localization systems could be a useful source for data collection beside the strong enhancement of human interaction. This paper is focused to help retailers and insiders for many purposes such as products development or improvement, segmentation strategies and human behaviour analyses into such stores where the embedded computing augment the environment.