In this paper we describe a system for automatic people counting in crowded environments. The approach we propose is a counting-by-detection method based on depth imagery. It is designed to be deployed as an autonomous appliance for crowd analysis in video surveillance application scenarios. Our system performs foreground/background segmentation on depth image streams in order to coarsely segment persons, then depth information is used to localize head candidates which are then tracked in time on an automatically estimated ground plane. The system runs in realtime, at a frame-rate of about 20 fps. We collected a dataset of RGB-D sequences representing three typical and challenging surveillance scenarios, including crowds, queuing and groups. An extensive comparative evaluation is given between our system and more complex, Latent SVM-based head localization for person counting applications.

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Performing face recognition across 3D scans of different resolution is now attracting an increasing interest thanks to the introduction of a new generation of depth cameras, capable of acquiring color/depth images over time. However, these devices have still a much lower resolution than the 3D high-resolution scanners typically used for face recognition applications. If data are acquired without user cooperation, the problem is even more challenging and the gap of resolution between probe and gallery scans can yield to a severe loss in terms of recognition accuracy. Based on these premises, we propose a method to build a higher-resolution 3D face model from 3D data acquired by a low-resolution scanner. This face model is built using data acquired when a person passes in front of the scanner, following an uncooperative protocol. To perform non-rigid registration of point sets and account for deformation of the face during the acquisition process, the Coherent Point Drift (CPD) method is used. Registered 3D data are filtered through a variant of the lowess method to remove outliers and build the final face model. The proposed approach is evaluated in terms of accuracy of face reconstruction and face recognition.

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