Artificial immune system optimization based duplex kinect skeleton fusion

Abstract

Human motion tracking, which requires both motion sensing hardware and algorithms based on computer vision, is an enjoyable and active research area with diverse applications. As a depth sensor device Kinect is a famous hardware component for this task. In this work, we studied using more than one Kinect camera to obtain better motion tracking which is applicable for motion capture. We synthetically created two camera data from one and then focused on de-noising and fusing these data in order to obtain more realistic skeleton joint coordinates. Artificial Immune System (AIS) optimization algorithm is suggested and used for this task. As a result we obtained 30% better fusion results from noisy synthetic data. Our results showed that AIS is a promising algorithm for obtaining optimal joint coordinates in the fusion of multiple Kinect skeleton data.