Robustly Tracking People with LIDARs in a Crowded Museum for Behavioral Analysis
Md. Golam Rashed, Ryota Suzuki, Takuya Yonezawa, Antony Lam, Yoshinori Kobayashi, Yoshinori Kuno · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · 2017
This introduces a method which uses LIDAR to identify humans and track their positions, body orientation, and movement trajectories in any public space to read their various types of behavioral responses to surroundings. We use a network of LIDAR poles, installed at the shoulder level of typical adults to reduce potential occlusion between persons and/or objects even in large-scale social environments. With this arrangement, a simple but effective human tracking method is proposed that works by combining multiple sensors' data so that large-scale areas can be covered. The effectiveness of this method is evaluated in an art gallery of a real museum. The result revealed good tracking performance and provided valuable behavioral information related to the art gallery.