Accurate indoor tracking using a mobile phone and non-overlapping camera sensor networks

Cameras are common in the public building, and become one of the popular sensors for the indoor tracking. However, a camera will produce much data and consume much energy. Consequently, the cameras, deployed in a large area, would have no overlapping field of views (fovs). This leads to we cannot determine the person´s position in unobserved area correctly, due to the person´s unpredicted motion change, such as taking turns. Smart phone-based dead reckoning (DR) method is recently observed to be an alternative for filling these cameras´ gaps to realize seamless tracking. Therefore, in this paper, we propose to accurately track a person in indoor environment by employing particle filtering, which fuses the absolute position results from cameras and the relative position results from the smart phone-based DR method. Experiment results demonstrate the performance of the proposed joint tracking system. As we expected, the dead reckoning´s drawbacks such as accumulative errors and the unavailability of the initial position, are solved by the cameras. As well, the positions´ estimations between the cameras are filled with the DR method.