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Scene parameters analysis of skeleton-based human detection for a mobile robot using Kinect

The need to have a system that able to detect and track human is very important as it can be used to develop human-following robot for various tasks. This paper describes the best parameters that can be utilized in human tracking using skeleton-based method. Kinect sensor is used as it is preferred by others due to its cost and performance. In this paper, different types of parameters are quantitatively measured in order to choose the optimum scene that can be used for tracking human using skeleton-based method. Parameters such as distances to the sensor, lighting intensity, and the presence of obstacle are selected as these are among the main factors that need to be considered while tracking human indoor. Statistical analysis is done to the collected data in order to make final justifications. It can be concluded that the ideal conditions for the Kinect to detect and track human using skeleton-based algorithm would be at a distance of 3 meters from the sensor, lightings of 158 lux, and with minimal or no obstacles at all. These conditions can be considered by others when adapting Kinect for human tracking using skeleton-based method, especially for indoor mobile robotics applications.