3D Sensing and Tracking of Human Gait

Abstract

Motion capture technology has been applied in many fields such as animation, medicine, military, etc. since it was first proposed in the 1970s. Based on the principles applied, motion capture technology is generally classified into six categories: 1) Optical; 2) Inertial; 3) Magnetic; 4) Mechanical; 5) Acoustic and 6) Markerless. Different from the other five kinds of motion capture technologies which try to track path of specific points with different equipment, markerless systems recognize human or non-human body's motion with vision-based technology which focuses on analyzing and processing the captured images for motion capture. The user doed not need to wear any equipment and is free to do any action in an extensible measurement area while a markerless motion capture system is working. Though this kind of system is considered as the preferred solution for motion capture, the difficulty for realizing an effective and high accuracy markerless system is much higher than the other technologies mentioned, which makes markerless motion capture development a popular research direction. Microsoft Kinect sensor has attracted lots of attention since the launch of its first version with its depth sensing feature which gives the sensor the ability to do motion capture without any extra devices. Recently, Microsoft released a new version of Kinect sensor with improved hardware and and targeted at the consumer market. However, to the best of our knowlege, the accuracy assessment of the sensor remains to be answered since it was released. In this thesis, we measure the depth accuracy of the newly released Kinect v2 depth sensor from different aspects and propose a trilateration method to improve the depth accuracy with multiple Kinects simultaneously. Based on the trilateration method, a low-cost, no wearable equipment requirement and easy setup human gait tracking system is realized.