Substraksi Latar Menggunakan Nilai Mean Untuk Klasifikasi Kendaraan Bergerak Berbasis Deep Learning

Ilal Mahdi, Kahlil Muchtar, Fitri Arnia, Tia Ernita


Moving object detection systems have been widely used in everyday life. Currently, research in the field of background subtraction is still being carried out to achieve maximum accuracy results. This study aims to model the background subtraction of an image using the mean value with the concept of non-overlapping block. Furthermore, the background abstraction results will be used in deep learning-based moving object detection. Specifically, the input image will be divided into several blocks, then the mean value of each block will be calculated to later produce a binary block (binary map). The binary blocks that have been generated will be used as input for background modeling. The background model aims to separate moving objects from the background in the input image. The resulting moving object (object localization) will be sent to the object classification stage using deep learning. The dataset used in this study is CDNet 2014. The results of the study were able to produce a more accurate moving object detection system. Quantitative tests carried out resulted in an accuracy of above 90%.


moving object detection; mean value; deep learning; background subtraction

Full Text:



C.H. Yeh, C.Y. Lin, K. Muchtar, and L.W. Kang, “Real-time background modeling based on a multi-level texture description”, Information Sciences, vol. 269, pp.106-127, 2014.

A. Farhadi., and J. Redmon., YOLOv4: An incremental improvement. In Computer Vision and Pattern Recognition, Berlin/Heidelberg, Germany: Springer, pp. 1804-2767, 2018.

K. Muchtar, F. Rahman, T.W. Cenggoro, A. Budiarto, and B. Pardamean, “An improved version of texture-based foreground segmentation: block-based adaptive segmenter”, Procedia Computer Science, vol. 135, pp.579-586, 2018.

S. Y. Irianto, Analisa Citra Digital dan Content Based Image Retrieval, 1st Ed, Bandar Lampung, Indonesia: CV. Anugrah Utama Raharja (AURA), 2016.

M. Heikkila dan M. Pietikainen, “A Texture-Based Method for Modeling the Background and Detectiong Moving Objects,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 657-661, 2006.

K. He, J. Sun dan X. Tang, "Guided Image Filtering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 6, 2013.

Solichin dan A. Harjoko, “Metode Background Subtraction untuk Deteksi Obyek Pejalan Kaki pada Lingkungan Statis,” Seminar Nasional Aplikasi Teknologi (SNATI), Yogyakarta, Indonesia, 2013.

A. Susanto, “Penerapan Operasi Morfologi Matematika Citra Digital Untuk Ekstraksi Area Plat Nomor Kendaraan Bermotor,” Jurnal Pseudocode, vol. 6, no. 1, 2019.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, and A.C. Berg, “Imagenet large scale visual recognition challenge”, International Journal of Computer Vision, vol. 115, no. 3, pp.211-252, 2015.

Y. Wang, P.M. Jodoin, F. Porikli, J. Konrad, Y. Benezeth, and P. Ishwar, P. “CDnet 2014: An expanded change detection benchmark dataset”, In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, USA, 2014, pp. 387-394.

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).

K. He, J. Sun, and X. Tang, Guided image filtering. In European

conference on computer vision, 2010, pp. 1-14. Springer, Berlin, Heidelberg.

Nasaruddin, K. Muchtar, dan Afdhal, “A Lightweight Moving Vehicle Classification System Through Attention-Based Method and Deep Learning,” IEEE Access, vol. 7, pp. 157564-157573, 2019.

N. Nasaruddin, K. Muchtar, A. Afdhal, and A. P. J. Dwiyantoro,

Deep anomaly detection through visual attention in surveillance

videos. Journal of Big Data, vol.7, no.1, pp.1-17, 2020.


Article Metrics

Abstract view : 0 times
PDF - 0 times


  • There are currently no refbacks.

View My Stats


Creative Commons License

Jurnal Rekayasa Elektrika (JRE) is published under license of Creative Commons Attribution-ShareAlike 4.0 International License.