Real-time RGB-D based people detection and tracking system for mobile robots
Fang Fang, Kun Qian, Bo Zhou, Xudong Ma · 2017
A real-time RGB-D based person detection and tracking system suitable is presented for mobile robots. Our approach combines RGB-D visual odometry estimation, target feature extraction, nearest point position information into a robust vision system that runs on open source robot operating system. As people detection is the most expensive component in any such integration, we invest significant effort into taking maximum advantage of the available depth information. Tracking process by European clustering method removes the noise information. When the depth information is not enough, it is reasonable to use the Cam-Shift method which is based on RGB information. Experimental results validate the feasibility and effectiveness of the proposed method.