Smart Campus Care and Guiding With Dedicated Video Footprinting Through Internet of Things Technologies

IEEE Access

Ming-Fong Tsai¹  Lien-Wu Chen²  Jun-Xian Liu²  Tsung-Ping Chen²  Da-En Chen² 

[1] Department of Electronic Engineering, National United University, Miaoli, Taiwan;Department of Information Engineering and Computer Science, Feng Chia University, Taichung, Taiwan;

关键词: Face detection;    face recognition;    indoor positioning;    Internet of Things;    mobile device;   

DOI  :  10.1109/ACCESS.2018.2856251

来源: DOAJ

【 摘 要 】

In this paper, we propose a smart campus care and guiding framework with deep learning-based face recognition, called DeepGuiding, for students through Internet of Things technologies. The DeepGuiding framework can construct the dedicated video trajectory of a campus student, where the recorded video for each student can be automatically classified to achieve efficient footprint review as necessary. In addition, DeepGuiding can provide time-efficient indoor and outdoor guiding in a campus to quickly reach places, meet friends, and find students. To the best of our knowledge, DeepGuiding is the first campus care and guiding system which provides the following features: 1) it achieves the seamless outdoor and indoor navigation between buildings in a campus; 2) it keeps additional construction cost low by utilizing existing surveillance cameras in a campus; and 3) it reduces the total searching time for finding a specific event/target in a campus by alleviating time-consuming labor overhead to review a huge amount of video data. An Android-based prototype using iBeacon indoor localization and global positioning system outdoor positioning with surveillance cameras is implemented to verify the feasibility and superiority of our DeepGuiding framework. The Experimental results show that DeepGuiding outperforms existing face recognition methods and can achieve high recognition accuracy for students not close to surveillance cameras.

【 授权许可】

Unknown