Person tracking association using multi-modal systems

In this paper, a novel multi-modal method for person identification in indoor environments is presented. This ap- proach relies on matching the skeletons detected by a Kinect v2 device with wearable devices equipped with inertial sen- sors. Movement features such as yaw and pitch changes are employed to associate a particular Kinect skeleton to a person using the wearable. The entire process of sensor cal- ibration, feature extraction, synchronization and matching is detailed in this work. Six detection scenarios were de- fined to assess the proposed method. Experimental results have shown a high accuracy in the association process