Analysis of Combination Knowledge Acquisition of Haar Training for Object Detection on the Viola Jones Method

Abstract

Viola Jones method uses the file classifier to object detection. The training process to create object classifier file requires very high computer resources and time which is directly proportional to the amount of training data. The amount of training data determines the accuracy of object detection. The long training process is caused because the computer has low specifications and the distribution of Haartraining files will speed up the process of vector file formation, minimize errors when cutting Haar features on positive objects and also minimize errors that occur during the training process. The problems that arise next are how to overcome this so that a better knowledge is obtained. This study provides analysis results of the process of merging knowledge acquisition and its effect on the accuracy of object detection using the Viola-Jones method with the final result undetected object decrease 52.62% and object detected increase 23.78%

Keywords

Knowledge Acquisition; Training; Object Detection; Viola-Jones

Full Text:

PDF (Bahasa Indonesia)

References

Jones, M., Viola, P. (2003) Fast multi-view face detection. Mitsubishi Electric Research Lab TR-20003-96, 3:14.

Viola, P., & Jones, M. J. (2004). Robust Real-Time Face Detection. International Journal of Computer Vision,57(2), 137-154. doi:10.1023/b:visi.0000013087.49260.fb.

Prasetya, D.A, (2012). Deteksi Wajah Metode Viola Jones Pada OpenCV Menggunakan Pemrograman Python. Simposium Nasional RAPI XI FT UMS.ISSN : 1412-9612.

Putro, M.D., Adji, T.B., Winduratna, B.(2012). Sistem Deteksi Wajah dengan Menggunakan Metode Viola-Jones. Seminar Nasional Science, Engineering and Technology.TIF09-3.

Suharso, Aries,.(2016).Pengenalan Wajah Menggunakan Viola-Jones dan Eigenface Dengan Variasi Posisi Wajah Bebasis Webcam. Vol. 1, No. 2, E-ISSN 2503-054X.

Sajati, H. (2018). The Effect of Peak Signal to Noise Ratio (PSNR) Values on Object Detection Accuracy in Viola Jones Method. Conference SENATIK STT Adisutjipto Yogyakarta,4, 167-174. doi:10.28989/senatik.v4i0.139

Sianturi, Jonatan. (2018). Sistem Pendeteksian Manusia untuk Keamanan Rungan Menggunkan Viola-Jone. Vol. 1, P-ISSN 2549-6247, E-ISSN 2549-6255.

Sun, X., Wu, P., & Hoi, S. C. (2018). Face detection using deep learning: An improved faster RCNN approach. Neurocomputing,299, 42-50. doi:10.1016/j.neucom.2018.03.030.

Moghimi, M. M., Nayeri, M., Pourahmadi, M., Moghimi, M. K. (2018). Moving vehicle detection using AdaBoost and haar-like feature in surveillance videos, International Journal of Imaging and Robotics, vol. 18, no. 1, pp. 94–106.

Kurniawan, F., Sajati, H., Dinaryanto, O., (2015), Pendeteksian Kepadatan Lalu-lintas dengan Menggunakan Simpangan Baku Histogram Citra Jalan. Prosiding Seminar Nasional ReTII ke-10 2015.

Li, S. Z., Zhu, L., Zhang, Z., Blake, A., Zhang, H., & Shum, H. (2002). Statistical Learning of Multi-view Face Detection. Computer Vision — ECCV 2002 Lecture Notes in Computer Science, 67-81. doi:10.1007/3-540-47979-1_5.

Sajati, H., Astuti, Y., (2013), Analisis Dan Perancangan Software Untuk Menentukan Warna Kendaraan Gelap Dan Terang. Jurnal Angkasa Vol. 5 No. 2.

Sajati, H., Astuti, Y., Octaviana, C.H., (2014), Analisis Pemrosesan Paralel Untuk Kompresi Video Pada Jaringan Komputer Berbasis IPv6. Jurnal Ilmiah Angkasa Vol. 6, No. 2.

Sajati, H. (2018), Analisis Kualitas Perbaikan Citra Menggunakan Metode Median Filter Dengan Penyeleksian Nilai Pixel, Jurnal Ilmiah Angkasa Vol. 10, No. 1, P-ISSN 2085-9503, E-ISSN 2581-1355.

Zein, A. (2018), Pendeteksian Kantuk Secara Real Time Menggunakan Pustaka OPENCV dan DLIB PYTHON, Sainstech Vol. 28, No. 2, pp. 22-26 ISSN 1410 - 7104

http://dx.doi.org/10.28989/compiler.v8i2.538

Article Metrics

Abstract view: 516 times
Download     : 354   times

This work is licensed under a Creative Commons Attribution 4.0 International License.

Refbacks

• There are currently no refbacks.