Data Fusion in a Multi Agent System for Person Detection and Tracking in an Intelligent Room

Abstract

The main components of a supervising system is detecting and tracking of the supervised person in an intelligent room. This paper presents architecture for a non-intrusive multi-agent system for person detection and tracking. The main objective of this system is to offer continuity over the user’s movement, as it can be controlled in such a way so as to keep the user inside the frame for most of the time. The proposed architecture will integrate different types of sensors: multiple Kinect sensors and a PTZ camera, in order to minimize the drawbacks of using only one type of sensor. For example field of view provided by Kinect sensor is not wide enough to cover the entire room. Also the PTZ camera is not able to detect and track a person in case of different special situations, such as the person is sitting or it is under the camera. Furthermore Kinect sensors will help the PTZ camera to control the camera’s orientation. Person detection and tracking is performed using computer vision techniques applied to RGB images. The system is designed over the existing platform AmI-Platform and is partially evaluated in the AmI-Lab laboratory from the University Politehnica of Bucharest (UPB).