Implementation of Object Detection Method for Intelligent Surveillance Systems at the Faculty of Engineering, Universitas Sebelas Maret (UNS) Surakarta

Aris Maulana Fauzan, Sutrisno Ibrahim, Meiyanto Eko Sulistyo

Abstract

The number of positive Covid-19 cases in Indonesia continue to increase. This increase influenced by the behavior of Indonesian citizens in dealing with the pandemic, one of which is rarely wearing masks. In this study, we implemented an object detection method for intelligent surveillance systems (ISS) at the Faculty of Engineering, Universitas Sebelas Maret (UNS), Surakarta. By implementing face detection and mask detection, the surveillance system can recognize whether a person in a CCTV video frame is wearing a mask or not. In addition, deep metric learning and histogram of gradient (HOG) are applied to recognize faces of unmasked people in images. The test results show that the surveillance system can recognize the use of masks with 75%-87% accuracy rate. Furthermore, the accuracy rate for facial recognition on images ranges from 69% -100% for each person

Full Text:

PDF

References

G. N. Sulman, T. Sanocki, D. Goldgof and R. Kasturi, "How effective is human video surveillance performance?," 19th International Conference on Pattern Recognition, 2008. ICPR 2008, Tampa, FL, 2008, pp. 1-3.R.

S. Ibrahim, “A comprehensive review on intelligent surveillance systems,” Communications in Science and Technology, vol. 1, pp. 7-14, 2016.

R.A. Shahad, M.H.M. Saad, A. Hussain, "Activity Recognition for Smart Building Application Using Complex Event Processing Approach, International Journal on Advanced Science, Engineering and Information Technology," vol. 8, pp. 315-322, 2018.

Kementrian Kesehatan. " Situasi Terkini Perkembangan Coronavirus Disease Covid-19." [Online]. Available: https://covid19.kemkes.go.id/situasi-infeksi-emerging/info-corona-virus/situasi-terkini-perkembangan-coronavirus-disease-covid-19-09-oktober-2020/#.X4AXfRIxXIU [Accesed: 9 October 2020]

Badan Pusat Statistik. "Perilaku Masyarakat di Masa Pandemi Covid-19." [Online]. Available: https://www.bps.go.id/publication/2020/09/28/f376dc 33cfcdeec4a514f09c/perilaku-masyarakat-di-masa-pandemi-covid-19.html [Accesed 9 October 2020]

M. Cristani, M. Farenzena, D. Bloisi, V. Murino, “Background Subtraction for Automated Multisensor Surveillance: A Comprehensive Review,” EURASIP Journal on Advances in Signal Processing, 24 pages, Volume 2010, 2010.

T. Bouwmans, F. El Baf, and B. Vachon, “Statistical background modeling for foreground detection: A survey,” in Handbook of Pattern Recognition and Computer Vision (Volume 4). Singapore: World Scientific, Jan. 2010, ch. 3, pp. 181–199.

M. Paul, S. M. Haque and S. Chakraborty, "Human detection in surveillance videos and its applications-a review", EURASIP Journal onar Advances in Signal Processing, vol. 2013, no. 1, pp. 1-16, 2013

M. Enzweiler and D. M. Gavrila, “Monocular pedestrian detection: Survey and experiments,” IEEE Trans. Pattern Analysis and Machine Intelligence, 2009. 1, 2, 6, 9, 14, 15, 18.

A. Yilmaz, M. Shah, Object tracking: A survey. Journal ACM Computing Surveys 38(4) (2006).

Arnold W. M. Smeulders, Dung M. Chu, Rita Cucchiara, Simone Calderara, Afshin Dehghan and, and Mubarak Shah, Visual Tracking: an Experimental Survey, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 36, NO. 7, July 2014.

T. Ko, "A survey on behavior analysis in video surveillance for homeland security applications," 37th IEEE Applied Imagery Pattern Recognition Workshop (AIPR '08), pp.1,8, 15-17 Oct. 2008. doi: 10.1109/AIPR.2008.4906450.

M. Cristani, R. Raghavendra, A. Del Bue, V. Murino, Human behavior analysis in video surveillance: A Social Signal Processing perspective, Neurocomputing, Volume 100, 16 January 2013, Pages 86-97, ISSN 0925-2312.

Wahyu Kurniawan, “Implementasi Metode People Tracking untuk Sistem Pengawasan Cerdas di Fakultas Teknik Universitas Sebelas Maret (UNS) Surakarta,” Final Project (2019).

Wahyu Kurniawan, Sutrisno Ibrahim, and Meiyanto Sulistyo, “People detection and tracking methods for intelligent surveillance system,” AIP Conference Proceedings 2217, 030110 (2020); https://doi.org/10.1063/5.0001022

Adnane Cabani, Karim Hammoudi, Halim Benhabiles, and Mahmoud Melkemi. "MaskedFace-Net--A Dataset of Correctly/Incorrectly Masked Face Images in the Context of COVID-19." arXiv preprint arXiv:2008.08016 (2020).

Prajna Bhandary. "Mask Classifier." [Online]. Available: https://github.com/prajnasb/observations [Accesed 15 September 2020]

DOI: https://doi.org/10.20961/jeeict.4.1.61197

Refbacks

  • There are currently no refbacks.