DOI: https://doi.org/10.21456/vol8iss1pp1-8

Deteksi dan Penggolongan Kendaraan dengan Kalman Filter dan Model Gaussian di Jalan Tol

*Raditya Faisal Waliulu  -  Universitas Muhammadiyah Sorong, Indonesia

Citation Format:
Abstract

Monitoring systems are widely implemented in various sectors aimed at improving the security and productivity aspects. The research aims to detect moving objects in the form of video file tipefile (* .avi) 640x480 resolution and image class according to pixel area. Moving objects are given in the Region of Interest path for easy detection. Detection on moving objects using methods of Kalman filter and gaussian mixture model. There are two types of distribution, the distribution of Background and Foreground. The form of the Foreground distribution is filtered using Bit Large Object segmentation to obtain the dimensions of the vehicle and morphological operations. The feature extraction results from the vehicle are used for vehicle classification based on pixel dimension. Segmentation results are used by Kalman Filter to calculate the tracking of moving object positions. If the Bit Large Object segmentation is not found moving object, then it is continued on the next frame. The final results of system detection are calculated using Positive True validation, True Negative, False Positive, and False Negative by looking for the sensitivity and specificity of each morning, day and night conditions

Fulltext View|Download
Keywords: Detection of moving objects; Recognition; Kalman filter; Gaussian mixture model

Article Metrics:

Article Info
Section: Research Articles
Language : EN
Recent articles
Penjadwalan Tenaga Kebidanan Menggunakan Algoritma Memetika Klasifikasi Opini Masyarakat Terhadap Jasa Ekspedisi JNE dengan Naïve Bayes Sistem Pendukung Keputusan Pemilihan Siswa Berprestasi Berbasis Website dengan Metode Simple Additive Weighting More recent articles
Most cited articles
Sistem Panel Kinerja Untuk Program Studi Sarjana Berbasis BAN PT Algoritma K-Means Clustering Untuk Pengelompokan Ayat Al Quran Pada Terjemahan Bahasa Indonesia Data Clustering Menggunakan Metodologi CRISP-DM Untuk Pengenalan Pola Proporsi Pelaksanaan Tridharma Sistem Informasi Geografis Berbasis Web untuk Pemetaan Sebaran Alumni Menggunakan Metode K-Means Penggunaan Jaringan Syaraf Tiruan Backpropagation Untuk Seleksi Penerimaan Mahasiswa Baru Pada Jurusan Teknik Komputer Di Politeknik Negeri Sriwijaya More cited articles
  1. Hue, C., Le Cadre,J.P dan Perez, P., 2000. Tracking multiple objects with particle filtering, Report Research, INRIA, Rennes
  2. Julier, S. J. Uhlmann, J. K., 2004. Unscented filtering and nonlinear estimation, Proceeding of the IEEE, 92 (3), 401–422
  3. MacCormick, J.P dan Blake, A., 2000. A probabilistic exclusion principle for tracking multiple objects. International Journal of Computer Vision 39 (1), 57–71
  4. Miller, R., Sun, Z.,Bebis, G., 2006. On Road vehicle Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (5), 694-711
  5. Raja, Y., McKenna, S.J, Gong, S., 1997. Segmentation and tracking using color mixture model. Proceeding Asian Conference on Computer Vision, Vol I, 607-614
  6. Schneiderman, H., Hoiem, D., Sukthankar, R., Huston, L., 2004. Object-based image retrieval using the statistical structure of images. Proceedings IEEE computer society conference on Computer vision and pattern recognition. Vol.2, pp 490-497
  7. Soh, Y.S, Hae, Y.S, Kim, I., 2012. Spatio-temporal gaussian mixture model for background modeling. IEEE International Symposium on Multimedia (ISM), 360-363
  8. Sun, Z., Bebis, G., Miller, R., 2006. On-road vehicle detection: a review", IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (5), 694-711
  9. Tao, H. Sawhney H.S, Kumar, R., 1999. A sampling algorithm for tracking multiple objects. Procceding of iternational Workshop on Vision Algorithms Corfu, Greece, September 21–22
  10. Viola, P., Jones, M.J., 2001. Rapid object detection using a boosted cascade of simple features. Proceedings of the Conference on Computer Vision and Pattern Recognition, Los Alamitos, CA, USA, 511-518
  11. Wei, Y., Jun, S., 2006. On Estimation and prediction for Multivariate Multiresolution Tree-Structured Spatial Linear Models, Statistica Sinica, 981-1020
  12. Welch, G dan Bishop, G., 2014. Kalman Filter. Computer Vision A Reference Guide, Springer, New York, 435-437
  13. Yadraj, M., Ajay, M., 2013. Blobs and Cracks detection on Plain Ceramic Tile Surface. International Journal of Advance Research in Computer Science and Software Engineering 3 (7), 647-652

Last update:

No citation recorded.

Last update: 2024-11-15 21:18:38

No citation recorded.