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In this paper we present an approach for counting and tracking people participating in a meeting that takes place in a smart room. The sensing and processing modules are incorporated within the context of an IoT framework, that follows a message oriented architecture. The proposed algorithm consists of a motion detection module, a background subtraction module, a people counting module and a tracking module, while its output is primarily used as input to a decision making module that controls the room's environment. We show that we may achieve satisfactory results, using simple low resolution cameras. We evaluate our method using a publicly available, real world data set. Experimental results on real life meetings indicate the effectiveness of our approach.

This paper presents an Internet-of-Things approach to fusing audio sensors towards the detection of audio events, in a meeting room scenario. The different types of audio sensors (microphones) and respective individual audio analysis modules are incorporated within the context of an IoT framework that follows a message-oriented architecture. Each individual audio analysis module is composed by a feature extraction stage and a Support-Vector-Machine (SVM) classifier. A fusion module is also adopted to combine the individual sensor-level decisions, in order to extract the final classification decision. A detailed experimental evaluation on a publicly available real-world dataset proves a rather significant performance boosting in terms of overall classification accuracy. In addition, the proposed architecture enables an easy-to-use training procedure that can easily handle any number of audio sensors and respective classifiers without any prior knowledge of the room's geometry or any other constraints regarding the topological condition of the sensors.