Performance Analysis of Illumination Invariant Change Detection Method for Detecting Image Change in Night Vision Camera

Abstract

At present, the use of video cameras is not only limited to documenting events but is also used for surveillance systems. Changes in lighting that occur in the surveillance area is one of the problems that result in a false alarm on the surveillance system. Illumination Invariant Change Detection is a method for detecting image changes on images. This study aims to determine the performance of the Illumination Invariant Change Detection method to detect image changes in night vision surveillance cameras. The Illumination Invariant Change Detection method does not work well for detecting image changes on a night vision camera under dark lighting conditions at an average value of Lux 0 with an infrared lamp on. The accuracy of the application of the method to detect image changes on night vision cameras is 80% with the selection of the threshold value of the detection of image changes that is 75000 pixels.

Keywords

image change detection, Illumination Invariant, Illumination Invariant Change Detection, night vision camera

Full Text:

PDF (Bahasa Indonesia)

References

Ki, M., Cho, B., Jeon, T., Choi, Y., & Byun, H. (2018, November). Face Identification for an in-vehicle Surveillance System Using Near Infrared Camera. In 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (pp. 1-6). IEEE.

Kusuma, H., Wirawan, W., & Suprijanto, A. (2015). Normalisasi Iluminasi Citra Wajah Dengan Menggunakan Histogram Remapping Pada Pengenalan Wajah Berbasis Fitur Gabor. JAVA Journal of Electrical and Electronics Engineering, 13(2).

Megantara, R. A., & Pramunendar, R. A. (2017). Pengembangan Background Subtraction Menggunakan FCM Untuk Deteksi Objek Bergerak Berdasarkan Pencahayaan Yang Bervariasi. Techno. Com, 16(4), 435-443.

Putri, A. R. (2016). Pengolahan Citra dengan Menggunakan Web CAM pada Kendaraan Bergerak Di Jalan Raya. Jurnal Ilmiah Penelitian dan Pembelajaran Informatika, 1(01).

Hadjkacem, B., Ayedi, W., Abid, M., & Snoussi, H. (2017, October). A new method of video-surveillance data analytics for the security in camera networks. In 2017 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC) (pp. 140-145). IEEE.

Malathi, T., & Bhuyan, M. K. (2013, April). Multiple camera-based codebooks for object detection under sudden illumination change. In 2013 International Conference on Communication and Signal Processing (pp. 310-314). IEEE.

Augustin, M. B., Juliet, S., & Palanikumar, S. (2011, March). Motion and feature based person tracking in surveillance videos. In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (pp. 605-609). IEEE.

Kim, I. S., Jeong, Y., Kim, S. H., Jang, J. S., & Jung, S. K. (2019, July). Deep Learning based Effective Surveillance System for Low-Illumination Environments. In 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 141-143). IEEE.

Choi, H., Dinh, Q., & Jeon, M. (2018, January). Robust relationship learning to illumination in a camera network. In 2018 International Conference on Electronics, Information, and Communication (ICEIC) (pp. 1-4). IEEE.

Wan, X., Liu, J., Li, S., Dawson, J., & Yan, H. (2018). An illumination-invariant change detection method based on disparity saliency map for multitemporal optical remotely sensed images. IEEE Transactions on Geoscience and Remote Sensing, 57(3), 1311-1324.

Wan, X., Liu, J., Qin, M., & Li, S. Y. (2018). ILLUMINATION INVARIANT CHANGE DETECTION (IICD): FROM EARTH TO MARS. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 42(3).

Kim, W. H., Yi, K., & Kyung, C. M. (2015). Energy-efficient illumination-invariant change detection in DCT coefficient domain for vehicular black box camera. Electronics Letters, 51(11), 822-824.

Priadana, A., & Harjoko, A. (2017). Deteksi Perubahan Citra Pada Video Menggunakan Illumination Invariant Change Detection. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 11(1), 89-98.

Dawson-Howe, K. (2014). A practical introduction to computer vision with opencv. John Wiley & Sons.

R. C. Gonzalez and R. E. Woods.(2018). Digital Image Processing, 4th ed. New York: Pearson

E. M. Martín and Á. P. del Pobil.(2012). Robust Motion Detection in Real-Life Scenarios, 1st ed. Springer-Verlag London.

http://dx.doi.org/10.28989/compiler.v8i2.514

Article Metrics

Abstract view: 507 times
Download     : 294   times

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

Refbacks

• There are currently no refbacks.