Person tracking association using multi-modal systems

In this paper, a novel multi-modal method for person identification in indoor environments is presented. This approach relies on matching the skeletons detected by a Kinect v2 device with wearable devices equipped with inertial sensors. 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 calibration, feature extraction, synchronization and matching is detailed in this work. Six detection scenarios were defined to assess the proposed method. Experimental results have shown a high accuracy in the association process.