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Gömülü bir platform üzerinde gerçek zamanlı şeritten ayrılma uyarı sistemi

Year 2017, Volume: 32 Issue: 4, 1287 - 1300, 08.12.2017
https://doi.org/10.17341/gazimmfd.369719

Abstract

Bu
çalışmada gömülü bir platform üzerinde gerçek zamanlı çalışan şeritten ayrılma
uyarı sistemi önerilmiştir. Önerilen sistemde sürücülerin sürüş sırasında
dikkatsizlik, uykuya dalmaları vb. nedenler ile aracın kontrolünü kaybederek
istemsiz bir şekilde şerit değiştirdikleri görüntü işleme yaklaşımları ile
tespit edilerek sürücüye uyarı verilmektedir. Bu çalışmada önerilen sistemde
öncelikle uygulanan bir filtre ile şerit işareti öne çıkarılarak, şerit işareti
tespit işleminin olası bozucu etkilere karşı gürbüz bir şekilde çalışması
sağlanmaktadır. Sonraki adımda filtrelenmiş giriş görüntüsü ile 1-D Gaussian
fonksiyonunun korelasyon işlemi sonucu şerit çizgisi aday noktaları
belirlenmektedir. Belirlenen bu aday noktalarından olası bozucular RANSAC
(RANdom SAmple Consensus) yöntemi kullanılarak elenmektedir ve şerit çizgileri
elde edilmektedir. Ayrıca tespit edilen şerit çizgilerinin konumlarının
zamansal ilişkisi Kalman filtresi ile incelenmektedir. Elde edilen şerit
çizgisi konumları ve önceden belirlenen araç konumu yorumlanarak aracın
şeritten ayrılma durumu tespit edilmektedir. Geliştirilen sistem 752×480
uzamsal boyutuna sahip giriş görüntüleri için 1 GHz frekansında ARM A8 işlemci
üzerinde 16 fps’de çalışmaktadır.

References

  • 1. COWI, Cost-benefit assessment and prioritization of vehicle safety technologies (TREN-ECON2-002), Avrupa Komisyonu Enerji ve Taşımacılık Genel Müdürlüğü, Brüksel-Belçika, 2006.
  • 2. Kaya H., Çavuşoğlu A., Çakmak H., Şen B., Delen D., Supporting the diagnosis process and processes after treatment by using image segmentation and image simulation techniques: Keratoconus example, Journal of the Faculty of Engineering and Architecture of Gazi University, 31 (3), 737-747, 2016.
  • 3. Avcı E., Tuncer T., Avcı D., A new data hiding algorithm based on minesweeper game for binary images, Journal of the Faculty of Engineering and Architecture of Gazi University, 3(4), 951-959, 2016.
  • 4. Selvi E., Selver M., Kavur A., Güzeliş C., Dicle O., Segmentation of abdominal organs from MR images using multi-level hierarchical classification, Journal of the Faculty of Engineering and Architecture of Gazi University, 30 (3), 533-546, 2015.
  • 5. Gaikwad V., Lokhande S., Lane departure identification for advanced driver assistance, IEEE Transactions on Intelligent Transportation Systems, 16 (2), 910-918, 2015.
  • 6. Duda R.O., Hart P.E., Use of the Hough transformation to detect lines and curves in pictures, Communications of the ACM, 15 (1), 11–15, 1972.
  • 7. Borkar A., Hayes M., Smith, M.T., A novel lane detection system with efficient ground truth generation, IEEE Transactions on Intelligent Transportation Systems, 13 (1), 365–374, 2012.
  • 8. Bertozzi M., Broggi A., GOLD: A parallel real-time stereo vision system for generic obstacle and lane detection, IEEE Transactions on Image Processing, 7 (1), 62-81, 1998.
  • 9. Fischler M.A., Bolles R.C., Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, 24 (6), 381–395, 1981.
  • 10. Kalman R.E., A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82 (1), 35-45, 1960.
  • 11. Tapia-Espinoza R., Torres-Torriti M., Robust lane sensing and departure warning under shadows and occlusions, Sensors, 3 (1), 3270-3298, 2013.
  • 12. Torr P.H.S., Zisserman A., MLESAC: A new robust estimator with application to estimating image geometry, Computer and Vision Image Understanding, 78 (1), 138–156, 2000.
  • 13. Yoo H., Yang U., Sohn K., Gradient-enhancing conversion for illumination-robust lane detection, IEEE Transactions on Intelligent Transportation Systems, 14 (3), 1083-1094, 2013.
  • 14. You F., Zhang R., Zhong L., Wang H., Xu J., Lane detection algorithm for night-time digital image based on distribution feature of boundary pixels, Journal of the Optical Society of Korea, 17 (2), 188-199, 2013.
  • 15. Otsu N., A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man and Cybernetics, 9 (1), 62-66, 1979.
  • 16. Mammeri A., Boukerche A., Tang Z., A real-time lane marking localization, tracking and communication system, Computer Communications, 73, 132-143, 2016.
  • 17. Matas J., Chum O., Urban M., Pajdla T., Robust wide baseline stereo from maximally stable extremal regions, Image and Vision Computing, 22 (10), 761-767, 2004.
  • 18. Stephens R.S., Probabilistic approach to the Hough transform, Image and Vision Computing, 9 (1), 66-71, 1991.
  • 19. Küçükyıldız G., Ocak H., Development and optimization of a DSP-based real-time lane detection algorithm on a mobile platform, Turkish Journal of Electrical Engineering and Computer Sciences, 22 (6), 1484-1500, 2012.
  • 20. Shin B., Tao J., Klette R., A superparticle filter for lane detection, Pattern Recognition, 48 (11), 3333-3345, 2015. 21. Nieto M., Laborda J.A., Salgado L., Road environment modeling using robust perspective analysis and recursive Bayesian segmentation, Machine Vision and Applications, 22 (6), 927-945, 2011.
  • 22. Aly M., Real time detection of lane markers in urban streets, IEEE Intelligent Vehicle Symposium, Hollanda, 7-12, 2008.
  • 23. Funk N., A study of the Kalman filter applied to visual tracking, Teknik Rapor, University of Alberta, 2003.
  • 24. Bar-Shalom Y., Birmiwal K., Variable dimension filter for maneuvering target tracking, IEEE Transactions on Aerospace and Electronic Systems, 18 (5), 621-629, 1982.
Year 2017, Volume: 32 Issue: 4, 1287 - 1300, 08.12.2017
https://doi.org/10.17341/gazimmfd.369719

Abstract

References

  • 1. COWI, Cost-benefit assessment and prioritization of vehicle safety technologies (TREN-ECON2-002), Avrupa Komisyonu Enerji ve Taşımacılık Genel Müdürlüğü, Brüksel-Belçika, 2006.
  • 2. Kaya H., Çavuşoğlu A., Çakmak H., Şen B., Delen D., Supporting the diagnosis process and processes after treatment by using image segmentation and image simulation techniques: Keratoconus example, Journal of the Faculty of Engineering and Architecture of Gazi University, 31 (3), 737-747, 2016.
  • 3. Avcı E., Tuncer T., Avcı D., A new data hiding algorithm based on minesweeper game for binary images, Journal of the Faculty of Engineering and Architecture of Gazi University, 3(4), 951-959, 2016.
  • 4. Selvi E., Selver M., Kavur A., Güzeliş C., Dicle O., Segmentation of abdominal organs from MR images using multi-level hierarchical classification, Journal of the Faculty of Engineering and Architecture of Gazi University, 30 (3), 533-546, 2015.
  • 5. Gaikwad V., Lokhande S., Lane departure identification for advanced driver assistance, IEEE Transactions on Intelligent Transportation Systems, 16 (2), 910-918, 2015.
  • 6. Duda R.O., Hart P.E., Use of the Hough transformation to detect lines and curves in pictures, Communications of the ACM, 15 (1), 11–15, 1972.
  • 7. Borkar A., Hayes M., Smith, M.T., A novel lane detection system with efficient ground truth generation, IEEE Transactions on Intelligent Transportation Systems, 13 (1), 365–374, 2012.
  • 8. Bertozzi M., Broggi A., GOLD: A parallel real-time stereo vision system for generic obstacle and lane detection, IEEE Transactions on Image Processing, 7 (1), 62-81, 1998.
  • 9. Fischler M.A., Bolles R.C., Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, 24 (6), 381–395, 1981.
  • 10. Kalman R.E., A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82 (1), 35-45, 1960.
  • 11. Tapia-Espinoza R., Torres-Torriti M., Robust lane sensing and departure warning under shadows and occlusions, Sensors, 3 (1), 3270-3298, 2013.
  • 12. Torr P.H.S., Zisserman A., MLESAC: A new robust estimator with application to estimating image geometry, Computer and Vision Image Understanding, 78 (1), 138–156, 2000.
  • 13. Yoo H., Yang U., Sohn K., Gradient-enhancing conversion for illumination-robust lane detection, IEEE Transactions on Intelligent Transportation Systems, 14 (3), 1083-1094, 2013.
  • 14. You F., Zhang R., Zhong L., Wang H., Xu J., Lane detection algorithm for night-time digital image based on distribution feature of boundary pixels, Journal of the Optical Society of Korea, 17 (2), 188-199, 2013.
  • 15. Otsu N., A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man and Cybernetics, 9 (1), 62-66, 1979.
  • 16. Mammeri A., Boukerche A., Tang Z., A real-time lane marking localization, tracking and communication system, Computer Communications, 73, 132-143, 2016.
  • 17. Matas J., Chum O., Urban M., Pajdla T., Robust wide baseline stereo from maximally stable extremal regions, Image and Vision Computing, 22 (10), 761-767, 2004.
  • 18. Stephens R.S., Probabilistic approach to the Hough transform, Image and Vision Computing, 9 (1), 66-71, 1991.
  • 19. Küçükyıldız G., Ocak H., Development and optimization of a DSP-based real-time lane detection algorithm on a mobile platform, Turkish Journal of Electrical Engineering and Computer Sciences, 22 (6), 1484-1500, 2012.
  • 20. Shin B., Tao J., Klette R., A superparticle filter for lane detection, Pattern Recognition, 48 (11), 3333-3345, 2015. 21. Nieto M., Laborda J.A., Salgado L., Road environment modeling using robust perspective analysis and recursive Bayesian segmentation, Machine Vision and Applications, 22 (6), 927-945, 2011.
  • 22. Aly M., Real time detection of lane markers in urban streets, IEEE Intelligent Vehicle Symposium, Hollanda, 7-12, 2008.
  • 23. Funk N., A study of the Kalman filter applied to visual tracking, Teknik Rapor, University of Alberta, 2003.
  • 24. Bar-Shalom Y., Birmiwal K., Variable dimension filter for maneuvering target tracking, IEEE Transactions on Aerospace and Electronic Systems, 18 (5), 621-629, 1982.
There are 23 citations in total.

Details

Subjects Engineering
Journal Section Makaleler
Authors

Ayhan Küçükmanisa

Oğuzhan Urhan 0000-0002-0352-1560

Publication Date December 8, 2017
Submission Date January 18, 2017
Acceptance Date February 20, 2017
Published in Issue Year 2017 Volume: 32 Issue: 4

Cite

APA Küçükmanisa, A., & Urhan, O. (2017). Gömülü bir platform üzerinde gerçek zamanlı şeritten ayrılma uyarı sistemi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 32(4), 1287-1300. https://doi.org/10.17341/gazimmfd.369719
AMA Küçükmanisa A, Urhan O. Gömülü bir platform üzerinde gerçek zamanlı şeritten ayrılma uyarı sistemi. GUMMFD. December 2017;32(4):1287-1300. doi:10.17341/gazimmfd.369719
Chicago Küçükmanisa, Ayhan, and Oğuzhan Urhan. “Gömülü Bir Platform üzerinde gerçek Zamanlı şeritten ayrılma Uyarı Sistemi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 32, no. 4 (December 2017): 1287-1300. https://doi.org/10.17341/gazimmfd.369719.
EndNote Küçükmanisa A, Urhan O (December 1, 2017) Gömülü bir platform üzerinde gerçek zamanlı şeritten ayrılma uyarı sistemi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 32 4 1287–1300.
IEEE A. Küçükmanisa and O. Urhan, “Gömülü bir platform üzerinde gerçek zamanlı şeritten ayrılma uyarı sistemi”, GUMMFD, vol. 32, no. 4, pp. 1287–1300, 2017, doi: 10.17341/gazimmfd.369719.
ISNAD Küçükmanisa, Ayhan - Urhan, Oğuzhan. “Gömülü Bir Platform üzerinde gerçek Zamanlı şeritten ayrılma Uyarı Sistemi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 32/4 (December 2017), 1287-1300. https://doi.org/10.17341/gazimmfd.369719.
JAMA Küçükmanisa A, Urhan O. Gömülü bir platform üzerinde gerçek zamanlı şeritten ayrılma uyarı sistemi. GUMMFD. 2017;32:1287–1300.
MLA Küçükmanisa, Ayhan and Oğuzhan Urhan. “Gömülü Bir Platform üzerinde gerçek Zamanlı şeritten ayrılma Uyarı Sistemi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 32, no. 4, 2017, pp. 1287-00, doi:10.17341/gazimmfd.369719.
Vancouver Küçükmanisa A, Urhan O. Gömülü bir platform üzerinde gerçek zamanlı şeritten ayrılma uyarı sistemi. GUMMFD. 2017;32(4):1287-300.