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GPU Programlama Tekniği ile Yüksek Performanslı Araç Takibi

Yıl 2017, Cilt: 10 Sayı: 3, 255 - 261, 31.07.2017
https://doi.org/10.17671/gazibtd.331036

Öz

Bu çalışmada, Grafik İşlemci Birimi (GPU) Programlaması kullanılarak
yüksek performanslı bir araç takip uygulaması geliştirilmiştir. GPU programlama ortamı olarak Birleşik
Hesap Cihazı Mimarisi (CUDA) kullanılmıştır. Uygulama, GeForce GT 630 ve
GeForce GTX 550Ti isimli iki farklı ekran kartı üzerinde test edilmiş ve farklı
GPU’lara sahip ekran kartlarının uygulamanın performansına olan etkisi incelenmiştir.
Ayrıca
uygulama MATLAB ortamında
Merkezi İşlemci Birimi (CPU) üzerinde de gerçeklenerek, CPU-GPU karşılaştırılması
yapılmış ve ayrıntılı sonuçlar sunulmuştur
.
Elde edilen sonuçlar, araç takibi işleminde GPU programlamanın kullanılmasının
yüksek performans kazanımı getirdiğini göstermiştir. 

Kaynakça

  • [1] Coifman, B., Beymer, D., McLauchlan, P., & Malik, J. (1998). A real-time computer vision system for vehicle tracking and traffic surveillance. Transportation Research, Part C, 6, 271-288. [2] Betke, M. T., Haritaoglu, E., & Davis, L. S. (2000). Real-time multiple vehicle detection and tracking from a moving vehicle. Machine Vision and Applications, 12, 69–83. [3] Derek, M. R. (2004). Tracking multiple vehicles using foreground, background and motion models. Image and Vision Computing 22, 143–155. [4] Rad, R., & Jamzad, M. (2005). Real time classification and tracking of multiple vehicles in highways. Pattern Recognition Letters, 26, 1597–1607. [5] Haag, M., & Nagel, H. H. (1999). Combination of edge element and optical flow estimates for 3D-model-based vehicle tracking in traffic image sequences. International Journal of Computer Vision, 35, 3, 295–319. [6] Kim, Z., & Malik, J. (2003). Fast vehicle detection with probabilistic feature grouping and its application to vehicle tracking. Proceedings of the Ninth IEEE International Conference on Computer Vision, 524–531. [7] Hu, W., Xiao, X., Xie, D., Tan, T., & Maybank, S. (2001,4). Traffic accident prediction using 3D model-based vehicle tracking. IEEE Transactions on Vehicular Technology, 53, 3, 677 - 694. [8] J.-W. Hsieh, S.-H. Yu, Y.-S. Chen, W.-F. Hu, Automatic Traffic Surveillance System for Vehicle Tracking and Classification, IEEE Transactions on Intelligent Transportation Systems, Vol. 7, No. 2, Haziran 2006. [9] Kastrinaki, V., Zervakis, M., & Kalaitzakis, K. (2003). A survey of video processing techniques for traffic applications. Image and Vision Computing, 21, 359–381. [10] Sivaraman, S., & Manubhai, M. (2013). Integrated lane and vehicle detection, localization, and tracking: a synergistic approach. IEEE Transactions on Transportation Systems, 14, 2, 906 - 917. [11] Do, V. D., & Woo, D.M. (2016). Optical flow on vehicle tracking under unpredictable environments. Advanced Science and Technology Letters, 126, 32-36. [12] Zhao, X., Dawson, D., Sarasua, W. A., & Birchfield, S.T. (2016). Automated traffic surveillance system with aerial camera arrays imagery: macroscopic data collection with vehicle tracking, American Society of Civil Engineers, 10.1061/(ASCE)CP.1943-5487.0000646. [13] Lee, G., Mallipeddi, R., & Lee, M. (2017). trajectory-based vehicle tracking at low frame rates. Expert Systems With Applications, 10.1016/j.eswa.2017.03.023. [14] Saxena, S., & Sharma, N. (2016). Parallel image processing techniques, benefits and limitations. Research Journal of Applied Sciences, Engineering and Technology, 12, 2, 223-238. [15] Güler, E., & Geçer, B. (2013). People Counting. http://www.ebubekirguler.com/goruntu-isleme-yontemleri-ile-insan-sayma/. [16] El-Azim, S. A., Ismail I., & El-Lati, H. A. (2002). An efficient object tracking technique using block-matching algorithm. Proc. Of the Nineteenth National, Radio Science Conf., 427-433. [17] Wren, C., Azarhayejani, A. Darrell, T., & Pentland, A.P. (1997). Pfinder: real-time tracking of the human body. IEEE Trans. on Pattern Analysis. And Machine Intelligence, 19, 7, 780-785. [18] Lo, B.P.L., & Velastin, S.A. (2001). Automatic congestion detection system for underground platforms. Proc. Of Int. Symposium on Intelligent Multimedia, Video and Speech Processing, 158-161. [19] Stauffer, C., & Grimson, W. E. L. (1999). Adaptive background mixture models for real-time tracking. Proceedings of conference on Computer Vision and Pattern Recognition, 2, 246-25. [20] Elgammal, A., Hanvood, D., & Davis, L.S. (2000). Nonparametric model for background subtraction. Proc. Of European Conf. on Computer Vision, 751-767. [21] Koller, D., Weber, J., Huang, T., Malik, J., Ogasawara, G., Rao, B., & Russell, S. (1994). Towards robust automatic traffic scene analysis in real-time, Proc. Of Int. Conf. on Pattern Recognition, 126-131. [22] Leoch, D. (2007). Real-time people counting system using video camera, Yüksek Lisans Tezi, Gjvik University College, 2007. [23] Gutchess, D., Trajkonic, M., Cohen-Solal, E., Lyons, D., & Jain, A. K. (2001). A background model initialization algorithm for video surveillance. The 8th IEEE Int. Conf. on Computer Vision, 733-740.
Toplam 1 adet kaynakça vardır.

Ayrıntılar

Bölüm Makaleler
Yazarlar

Yasemin Poyraz Bu kişi benim

Selçuk Sevgen

Yayımlanma Tarihi 31 Temmuz 2017
Gönderilme Tarihi 26 Temmuz 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 10 Sayı: 3

Kaynak Göster

APA Poyraz, Y., & Sevgen, S. (2017). GPU Programlama Tekniği ile Yüksek Performanslı Araç Takibi. Bilişim Teknolojileri Dergisi, 10(3), 255-261. https://doi.org/10.17671/gazibtd.331036