Human detection system by fusing depth map-based method and convolutional neural network-based method

In this paper, the depth images and the colour images provided by Kinect sensors are used to enhance the accuracy of human detection. The depthbased human detection method is fast but less accurate. On the other hand, the faster region convolutional neural network-based human detection method is accurate but requires a rather complex hardware configuration. To simultaneously leverage the advantages and relieve the drawbacks of each method, one master and one client system is proposed. The final goal is to make a novel Robot Operation System (ROS)-based Perception Sensor Network (PSN) system, which is more accurate and ready for the real time application. The experimental results demonstrate the outperforming of the proposed method compared with other conventional methods in the challenging scenarios.