Motion Detection and Face Recognition for CCTV Surveillance System

https://doi.org/10.22146/ijccs.18198

Ade Nurhopipah(1*), Agus Harjoko(2)

(1) Department of Informatics Technology, STMIK Amikom Purwokerto
(2) Department of Electronics and Computer Science, FMIPA UGM, Yogyakarta
(*) Corresponding Author

Abstract


Closed Circuit Television (CCTV) is currently used in daily life for a variety purpose. Development of the use of CCTV has transformed from a simple passive surveillance into an integrated intelligent control system. In this research, motion detection and facial recognation in CCTV video is done to be a base for decision making to produce automated, effective and efficient integrated system.

This CCTV video processing provides three outputs, a motion detection information, a face detection information and a face identification information. Accumulative Differences Images (ADI) used  for motion detection, and Haar Classifiers Cascade used  for facial segmentation. Feature extraction is done with Speeded-Up Robust Features (SURF) and Principal Component Analysis (PCA). The features was trained by Counter-Propagation Network (CPN).

Offline tests performed on 45 CCTV video. The test results obtained a motion detection success rate of 92,655%, a face detection success rate of 76%, and a face detection success rate of 60%. The results concluded that the process of faces identification through CCTV video with natural background have not been able to obtain optimal results. The motion detection process is ideal to be applied to real-time conditions. But in combination with face recognition process, there is a significant delay time.


Keywords


ADI; Haar Cascade Classifiers; SURF; PCA; CPN

Full Text:

PDF


References

[1] Padaruth, S., Indiwarsingh, F. & Bhugun, N., 2013, A Unified Intrusion Alert System using Motion Detection and Faces Recognition, 2nd International Conference on Mechine Learning and Computer Science (IMLCS), Kuala Lumpur.

[2] Namrata, Pradeep & Sagar, R., 2013. Cognitive Security System Based on Image Comparison and Motion Detection with Able Memory Usage. International Journal of Advances in Engineering & Technology, VI(2), pp.850-61.

[3] Putro, M.D., Adji, T.B. & Winduratna, B., 2012, Sistem Deteksi Wajah dengan Menggunakan Metode Viola-Jones, Proseding Seminar Nasional "Science, Engineering and Technology". Malang.

[4] Santoso, H. & Harjoko, A., 2013. Haar Cascade Classifier dan Algoritma Adaboost untuk Deteksi Banyak Wajah dalam Ruang Kelas. Jurnal Teknologi, VI(2), pp.108-15.

[5] Anand, B. & Shah, P.K., 2016. Face Recognation using SURF Features and SVM Classifier. International Journal of Electronics Engineering Research, VIII(1), pp.1-8.

[6] Balcoh, N.K., Yousaf, M.A., Ahmad, W. & Baig, M.I., 2012. Alghoritm for Efficient Attendence Management: Face Recognition Based Approach. International Journal of Computer of Science Issues (IJCSI), IX(4), pp.146-50.

[7] Febrianto, A.J., 2012, Pengenalan Wajah Dengan Metode Principle Component Analysis (PCA) Pada sistem Absensi Real Time, Tesis, Magister Teknik Elektro, Universitas Gadjah Mada, Yogyakarta.

[8] Tharanga, J.G.R., Samarakoon, S.M.C. & Karunarathne, T.A.P., 2013, Smart Attendance using Real Time Face Recognation (Smart-FR), SAITM Research Symposium on Engineering Advancement, Sri Lanka.

[9] Lwin, H.H., Khaing, A.S. & Tun, H.M., 2015. Aotomatic Door Access System Using Face Recognation. International Journal of Scientific & Technology Research , 4(06), pp.294-99.

[10] Lienhart, R. & Maydt, J., 2002, An Extended Set of Haar-like Features for Rapid Object Detection, International Conference on Image Processing., Institute of Electrical and Electronics Engineers (IEEE).

[11] Hecht-Nielsen, R., 1987. Counterpropagation networks. Optical Society of America, XXVI(23), pp.4979-84.

[12] Gonzalez, R.C. & Woods, R.E., 2008, Digital Image Processing, 3rd ed Prentice Hall, New Jersey.

[13] Fausett, L., 1994, Fundamentals of Neural Networks Architectures, Alghorithms and Applications, Prentice Hall, New Jersey.



DOI: https://doi.org/10.22146/ijccs.18198

Article Metrics

Abstract views : 12492 | views : 7684

Refbacks

  • There are currently no refbacks.




Copyright (c) 2018 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

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



Copyright of :
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 1978-1520 (print); ISSN 2460-7258 (online)
is a scientific journal the results of Computing
and Cybernetics Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijccs.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijccs



View My Stats1
View My Stats2