Distributed Sensor Networks for Multiple Human Recognition in Indoor Environments

Abstract

In this paper, we propose distributed sensor networks (DSNs) capable of performing reliable recognition targeted at multiple humans in the indoor environments. DSNs are composed with combinations of perception sensor units using a RGB-D sensor and a pan-tilt-zoom camera, and a control board to acquire 3W results of who, where, and what information based on audio-visual perception modules. In addition, fusion methods are utilized to associate with multiple human detection and tracking, face identification, and daily activity recognition. By evaluating the performance of DSNs in a classroom setting, it was confirmed that the proposed system can help to perform the tasks of various purposes.