Browse > Article
http://dx.doi.org/10.6109/jkiice.2020.24.11.1546

Development of Remote-Controlled Object-Recognizing Mobile Home CCTV Using Smartphone and Arduino  

Kim, Dong-Ju (Department of Electrical and Electronic Control Engineering, Hankyong National University)
Lim, Chae-Won (Department of Electrical and Electronic Control Engineering, Hankyong National University)
Choi, Hyun-Ho (School of ICT, Robotics & Mechanical Engineering, Hankyong National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.11, 2020 , pp. 1546-1549 More about this Journal
Abstract
This paper introduces the development process of mobile home CCTV that enables remote control and object recognition using unused smartphones and Arduino. Clients can control motors connected to Arduino through button, enable bidirectional voice communication between client-server and receive video from the server in real time. The server sends a PUSH notification to the client when its battery is low. When the server recognizes the charger, the client's remote control allows the server to dock to the charger and charge it. It was confirmed that video and voice delivery between client and server works well without any problems, and that object recognition works smoothly.
Keywords
Mobile home CCTV; Remote-Control; Object-Recognizing; Server-Client Communication;
Citations & Related Records
연도 인용수 순위
  • Reference
1 12 Uses for Your Old Smartphone. [Internet]. Available: https://www.pcmag.com/news/11-uses-for-your-old-smartphone/.
2 M. J. Donahoo, TCP/IP socket programming Java version, SciTech Media, 2002.
3 D.-H. Cho, Smartphone, Bluetooth, Ethernet, WiFi, and Arduino, Bogdoo Publisher, 2016.
4 S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, "Face recognition: A convolutional neural-network approach," IEEE Transactions on Neural Networks, vol. 8, no. 1, pp. 98-113, Jan. 1997.   DOI
5 N. Buduma, Fundamentals of Deep Learning, O'Reilly Media, 2017.
6 Smart Appbot video demonstration link. [Internet]. Available: https://youtu.be/jbGygU_3E5Y
7 A. Singh and R. Bhadani, Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter, Packt(GCO Science), 2020