Mobility in large touristic cities (such as Rome and Venice), where needs of citizen and tourists are different(and sometimes even conflicting), is a very relevant problem and info mobility is thus increasingly important. Since active technologies, requiring the passengers to wear some devices(e.g., RFID devices) are not commonly available and cannot be enforced on citizens and tourists, a complete passive sensor system is needed. In this paper we describe development and experimentation of techniques for human activity recognition for info mobility applications based on 3D data extracted from stereo and Kinect cameras. More specifically, we considered the problem of automatic estimation of the number of people present in a bus stop area in a crowded city, like Venice and experimented an approach integrating 3D data analysis, feature extraction and machine learning techniques. Results assessing the feasibility and performance of the proposed approaches are also presented in this paper.
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