Linking Computer Vision with Off-the-Shelf Accelerometry through Kinetic Energy for Precise Localization

Abstract

In this paper we propose the integration of computer vision with accelerometry in order to provide a precise localization solution. In terms of accelerometry, our approach makes use of a single off-the-shelf accelerometer on the waist to precisely obtain the velocity of the user. This allows us to calculate the kinetic energy of the person being tracked, and link the accelerometry data with the computer vision part of the system, where we employ segmentation of local regions of motion in the motion history image to estimate movement, and we leverage the number of pixels within the movement silhouettes as a metric accounting for the kinetic energy and the distance to the camera for the person being tracked. The fusion of the data from both technologies with a Kalman filter delivers an accuracy in the localization solution of up to 0.5 meters.