Multiple Person Tracking Based on Gait Identification Using Kinect and OpenPose

A gait provides the characteristics of a person’s walking style and hence is classified as personal identifiable information. There have been several studies for personal identification using gait, including works using hardware such as depth sensors and studies using silhouette image sequences of gait. However, these methods were designed specialized for tracking a single walking person and the accuracy reduction when multiple people are simultaneously reflected in several angles of view is not clear yet. In addition, dependencies on hardware-based methods is not clarified yet. In this study, we focus on Kinect and OpenPose, the representative gait identification techniques with a function to detect multiple people simultaneously in real time. We investigate how many people can be identified for these devices and with the accuracy for tracking.