Sistem Keamanan Gedung Menggunakan Kinect Xbox 360 Dengan Metode Skeletal Tracking
Building Security System Using Kinect Xbox 360 With Skeletal Tracking Method
Abstract
The incidence of fire and theft is very threatening and causes disruption to people's lifestyles, both due to natural and human factors resulting in loss of life, damage to the environment, loss of property and property, and psychological impacts. The purpose of this study is to create a building security system using Kinect Xbox 360 which can be used to detect fires and loss of valuable objects. The data transmission method uses the Internet of Things (IoT) and skeletal tracking. Skeletal detection uses Arduino Uno which is connected to a fire sensor and Kinect to detect suspicious movements connected to a PC. Kinect uses biometric authentication to automatically enter user data by recognizing objects and detecting skeletons including height, facial features and shoulder length. The ADC (Analog to Digital Converter) value of the fire sensor reading has a range between 200-300. The fire sensor detects the presence of fire through optical data analysis containing ultraviolet, infrared or visual images of fire. The data generated by Kinect by detecting the recognition of the skeleton of the main point of the human body known as the skeleton, where the reading point is authenticated by Kinect from a range of 1.5-3 meters which is declared the optimal measurement, and if a fire occurs, the pump motor will spray water randomly. to extinguish the fire that is connected to the internet via the wifi module. The data displayed is in the form of a graph on the Thingspeak cloud server service. Notification of fire and theft information using the delivery system from input to database
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References
zhu, s., hu, l., wu, l., & mao, s. (2011). An Improved Algorithm for Smoke Layer. Eighth International Conference on Fuzzy Systems and Knowledge Discovery, 410-413.
DOI: 10.1109/FSKD.2011.6019598
Ren, H., Chen, W., Ren, H., He, Y., & Chang, N. (2009). The Application of Fire Risk Evaluation Method in Fire. International Conference on Information Engineering and Computer Science, 1-4. DOI: 10.1109/ICIECS.2009.5364965
Tri, W. K., Nurcahyo, R., & Dachyar, M. (2018). Jakarta Fire Safety System Management Practices for. International Conference on Engineering Technologies, 1-4.
DOI: 10.1109/ICETAS.2018.8629119
ZHANG, Q., & YANG, X. T. (2014). Numerical Simulation and Detection Response Analysis of Fire in a Large Space. International Conference on Intelligent Computation Technology and Automation, 388-391. DOI: 10.1109/ICICTA.2014.100
Yunhong, L., & Meini , Q. (2016). The Design of Building Fire Monitoring System Based on ZigBee-WiFi Networks . Eighth International Conference on Measuring Technology and Mechatronics Automation, 733-735.
DOI: 10.1109/ICMTMA.2016.180
Rizan, O., & Hamidah. (2016). Rancangan Aplikasi Monitoring Kamera Cctv Untuk Perangkat Mobile Berbasis Android. 45-51.
http://jurnal.atmaluhur.ac.id/index.php/TI_atma_luhur/article/view/220/182
Liou, S. W., Qiu, G. Z., Zu, B. C., Jong, G. J., Kung, Y. F., & Hao, Z. W. (2018). Home Monitoring System Based Internet of Things. International Conference on Applied System Innovation, 325-327. DOI: 10.1109/ICASI.2018.8394599
Satapathy, L. M., Bastia, S. K., & Mohanty, N. (2018). Arduino based home automation using Internet of things (IoT). 118(17), 1-10.https://ieeeprojectsmadurai.com/IEEE%202019%20IOT%20BASEPAPERS/36_IOT%20BASED%20HOME%20AUTOMATION.pdf
Reddy, D. R., Goud, G. C., & Naidu , D. (2019). Internet of Things Based Pothole Detection System using Kinect Sensor. International Conference on I-SMAC, 232-236. DOI: 10.1109/I-SMAC47947.2019.9032694
Sabale, A. S., & Vaidya, Y. M. (2016). Accuracy Measurement of Depth Using Kinect Sensor. Conference on Advances in Signal Processing (CASP), 155-159. DOI: 10.1109/CASP.2016.7746156
Kolambe, P., Pote, R., Jadhav, A., & Chennur, V. (2018). Spy Robot With Fire Detection and Water Sprinkling. 1844-1848. DOI: 10.1109/ICECA.2018.8474617
Wilson, S., Varghese, S. P., A, N. G., I, M., & G, R. P. (2018). A Comprehensive Study on Fire Detection. Conference on Emerging Devices and Smart Systems (ICEDSS), 242-246. 10.1109/ICEDSS.2018.8544329
Southwell, B. J., & Fang, G. (2013). Human Object Recognition Using Colour and Depth Information from an RGB-D Kinect Sensor. International Journal of Advanced Robotic Systems, 10(171), 1-8.https://journals.sagepub.com/doi/pdf/10.5772/55717
Uroidhi, A., Mardiyanto, R., & Purwanto, D. (2017, January monday). Sistem pemetaan menggunakan fitur depth sensor kinect pada mobile robot untuk proses evakuasi kebakaran gedung. Diambil kembali dari jurusan teknik elektro fakultas teknik industri institut teknologi sepuluh nopember surabaya. https://repository.its.ac.id/2868/2/2214204003-Master_Theses.pdf
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