Intelligent Stereo Video Monitoring System for Paramedic Helmet

• Main Author: Liu, Yang
• Other Authors: El Saddik, Abdulmotaleb
• Language: en
• Published: Université d'Ottawa / University of Ottawa 2017
• Subjects: Vision; Stereo Vision; Embedded System; Paramedic
• Online Access: http://hdl.handle.net/10393/36652; http://dx.doi.org/10.20381/ruor-20932

During the first aid process, when patients are threatened by poor medical conditions, ambulance paramedics are required to administer emergency treatment based on instruc- tions provided by a remote emergency doctor through voice communication. However, such voice communication is always limited in expressing abundant detailed information for the patient. This thesis presents a framework for a stereoscopic and intelligent telemedicine sys- tem that can provide 3D live video communication between paramedics and emergency doctors. The proposed system captures 3D video from the paramedic headset carried by the paramedics, transmits the video through wireless live streaming, and displays the video with a 3D effect for emergency doctors in the hospital. The video can be analyzed to extract information about the patient through embedded algorithm such as face de- tection algorithm. In this thesis, the hardware, functional mechanism and face detection algorithm are introduced separately. The hardware of the system consists of a paramedic headset, a server box and a 3D PC, which are used to capture 3D video, transmit video through live streaming and display video with a stereo effect, respectively. The functional mechanism includes two subsystems, which work for pushing the stereo video to multiple live streams and displaying the 3D video from the live stream. In order to detect the patient information from the video, a multi-task face detection algorithm is applied to analyze the stereo video using deep learning technology. We improved the neural networks of face detection by utilizing 1 ⇥ 1 convolutional layers and retrain the network based on the transfer learning to achieve better and faster performance. This system has achieved good and stable performance in network delay (0.0489ms) and objective video quality evaluations. The face detection algorithm has achieved no- table accuracy (91.78% In FDDB dataset) and efficiency (19.71 ms/frame).