Evaluasi Metode Ekstraksi Fitur Hu Moment Invariants untuk Pengenalan Aktivitas Manusia

Authors

  • Hans Christian Kurniawan Departemen Informatika, Institut Teknologi Harapan Bangsa
  • Kevin Suryajaya Soemarto Departemen Informatika, Institut Teknologi Harapan Bangsa
  • Bernardo Nugroho Yahya Department of Industrial and Management Engineering, Hankuk University of Foreign Studies, South Korea

DOI:

https://doi.org/10.61769/telematika.v15i2.367

Keywords:

Human Activity Recognition (HAR), Hu Moment Invariants, Zernike Moment, HOG, Gait Pattern, SVM

Abstract

Vision-based Human Activity Recognition has been widely used due to a bunch of video data availability in the present days through CCTV and another mechanism which contains some human activities. This data is going to be very useful to improve and automate decision-making in several fields including security surveillance. In this field, it is important to achieve a good performance (i.e., accuracy) inefficient computational time. While there are many approaches in this field, most complex approaches require high computational time. In this work, we are evaluating Hu Moments performance, as well as being compared to other methods (i.e., Zernike Moment and Histogram of Oriented Gradient) by its accuracy and computational time. We also improved HAR flow by adding image denoising which has proven effective in increasing accuracy. The testing process includes videos that contain human activities such as walking, jogging, and running. The result shows that Hu Moments is superior among other methods, however there’s also some room for improvements found through this experiment.

 

 

Dalam era di mana terdapat banyak data video yang berisi aktivitas manusia, baik melalui rekaman CCTV maupun mekanisme lain, data tersebut menjadi sangat berharga untuk dapat diproses untuk pengenalan aktivitas manusia, atau Human Activity Recognition (HAR) yang dapat membantu pengambilan keputusan, di antaranya security surveillance. Untuk itu, diperlukan akurasi yang tinggi dan waktu komputasi yang efisien. Meskipun telah banyak metode di ranah ini, suatu teknik yang kompleks pada umumnya membutuhkan waktu komputasi yang tinggi. Dalam penelitian ini, dilakukan evaluasi dengan menggunakan metode Hu Moments yang akan dibandingkan dengan metode lainnya, yaitu Zernike Moment dan Histogram of Oriented Gradient (HOG), untuk segi akurasi dan waktu komputasinya. Ditambahkan juga tahap image denoising yang mampu meningkatkan akurasi. Proses pengujian menggunakan berbagai data video aktivitas manusia yang meliputi: berjalan, joging, dan berlari. Hasil riset menunjukkan bahwa metode Hu Moments memiliki performa yang lebih unggul dibandingkan metode ekstraksi fitur lainnya. Berdasarkan eksperimen yang dilakukan, terdapat beberapa area yang masih dapat ditingkatkan, untuk penelitian selanjutnya.

Author Biographies

Hans Christian Kurniawan, Departemen Informatika, Institut Teknologi Harapan Bangsa

Menerima gelar Sarjana Teknik Informatika dari Institut Teknologi Harapan Bangsa (ITHB) Bandung pada tahun 2016 dan Magister Informatika dari Institut Teknologi Bandung pada tahun 2019. Saat ini aktif sebagai Pengajar di ITHB serta sebagai Engineering Lead di Bukalapak.

Kevin Suryajaya Soemarto, Departemen Informatika, Institut Teknologi Harapan Bangsa

Menerima gelar Sarjana Komputer  dari Jurusan Informatika, Institut Teknologi Harapan Bangsa (ITHB) Bandung pada tahun 2020 dan saat ini bekerja sebagai IT Engineer.

Bernardo Nugroho Yahya, Department of Industrial and Management Engineering, Hankuk University of Foreign Studies, South Korea

Menerima gelar Master of Information System Engineering dari Dongseo University, Korea Selatan pada tahun 2004 dan Ph.D. di jurusan Industrial Engineering di Pusan National University, Korea Selatan tahun 2012. Saat ini aktif sebagai Professor di Departemen Teknik dan Manajemen Industri, Hankuk University of Foreign Studies, Korea Selatan.

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Published

2021-02-28

Issue

Vol. 15 No. 2 (2020)

Section

Articles

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