Kombinasi Deteksi Objek, Pengenalan Wajah dan Perilaku Anomali menggunakan State Machine untuk Kamera Pengawas

MUHAMMAD FAUZI NURYASIN, CARMADI MACHBUB, LENNI YULIANTI

Sari


ABSTRAK

Saat ini sistem kamera pengawas mengandalkan manusia dalam melakukan penerjemahan pada rekaman gambar yang terjadi. Perkembangan computer vision, machine learning, dan pengolahan citra dapat dimanfaatkan untuk membantu peran manusia dalam melakukan pengawasan. Penelitian ini merancang sistem kerja kamera yang terdiri dari tiga modul yaitu deteksi objek, pengenalan wajah, dan perilaku anomali. Deteksi objek memakai HOG-SVM, pengenalan wajah menggunakan CNN dengan arsitektur VGG-16 memanfaatkan transfer learning, dan perilaku anomali memakai spatiotemporal autoencoder berdasarkan threshold. Ketiga modul tersebut diuji menggunakan metrik akurasi, presisi, recall, dan f1-score. Ketiga modul diintegrasikan dengan state machine menjadi satu kesatuan sistem. Kinerja modul memiliki akurasi 88% untuk deteksi objek, 98% untuk pengenalan wajah, dan 78% untuk perilaku anomali. Hasil tampilan riil dapat diakses secara sederhana dan nirkabel melalui web.

Kata kunci: HOG-SVM, CNN, VGG-16, spatiotemporal autoencoder, state machine


ABSTRACT

Nowadays, the surveillance camera system relies on human to interpret the recorded images. Computer vision, machine learning, and image processing can be utilized to assist the human role in supervising. This study designed a camera work system consisting of three main modules, namely object detection, face recognition, and anomaly behavior. Object detection used the HOG-SVM combination. Facial recognition used CNN with the VGG-16 architecture that utilized transfer learning. Anomalous behavior used spatiotemporal autoencoder based on threshold. Modules are tested using the metrics of accuracy, precision, recall, and f1-score. The three modules are integrated using a state machine into one system. The performance of the module had 88% accuracy for object detection, 98% for facial recognition, and 78% for anomalous behavior. Real time video recording can be accessed wireless via web-based.

Keywords: HOG-SVM, CNN, VGG-16, spatiotemporal autoencoder, state machine


Kata Kunci


HOG-SVM; CNN; VGG-16; Spatiotemporal Autoencoder; state machine

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Referensi


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DOI: https://doi.org/10.26760/elkomika.v11i1.86

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ISSN (print) : 2338-8323 | ISSN (electronic) : 2459-9638

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