Abstract
In recent years, security has played a significant role in our daily lives. House security has become more popular with the enhancement of the Internet of Things (IoT) in intelligent home automation. This paper aims to build a system that can detect faces and the presence of unknown people using computer vision methods combined with the Internet of things. Two algorithms, Histogram of Oriented Gradients (HOG) and Haar Cascade classifiers with the enhancement of Convolutional Neural Network (CNN), were used to compare their performance. The system utilizes Nvidia Jetson Nano as an edge device, HD camera, Light Emitting Diode (LED), and sound buzzer as peripheral hardware. Telegram messenger application was used to notify the homeowner when uninvited guests passed through the pre-defined security area. In the result, the HOG method has 100% accuracy compared to 75% Haar Cascade with 720p video quality in the proposed developed system.
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Asy’ari, M.Z., Filbert, S., Sukra, Z.L. (2023). Histogram of Oriented Gradients (HOG) and Haar Cascade with Convolutional Neural Network (CNN) Performance Comparison in the Application of Edge Home Security System. In: Mukhopadhyay, S.C., Senanayake, S.N.A., Withana, P.C. (eds) Innovative Technologies in Intelligent Systems and Industrial Applications. CITISIA 2022. Lecture Notes in Electrical Engineering, vol 1029. Springer, Cham. https://doi.org/10.1007/978-3-031-29078-7_2
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