Person tracking on Kinect images using particle filter
Atsushi Yoshida, Hyoungseop Kim, Joo Kooi Tan, Seiji Ishikawa · 2014
In recent years, technique that estimates the movement of people from images has been studied actively. It is a technique to recognize and understand the intentions of human behaviors, using the results of sensing the state of a person by the time. It is widely applied for example to recognize gesture command of user or to detect suspicious persons using surveillance camera. Moreover, recent studies using Kinect is thriving. Kinect is a device that performs tracking and posture estimation of persons. However, the system has some problems. One of them is that Kinect's tracking system is vulnerable to occlusion. It is necessary to be improved. In this paper, we perform tracking of a person using a particle filter algorithm. We use a feature quantity obtained by combining the color information and depth information as the criteria of the particle filtering. We perform experiments at two situations. And, the results of the proposed method are compared with the results of the conventional method [6]. In certain circumstances, the results indicate that our proposed method is more accurate than the conventional method.