Moving Object Detection in Turbulence Degraded Video

Yıl 2015, Cilt: 3 Sayı: 4, 232 - 236, 04.12.2015

Nafiz Arica Tufan Caliskan

https://doi.org/10.18100/ijamec.97614

Öz

Atmospheric turbulence causes blurring and geometrical distortions in images acquired from a long distance. It makes it difficult to detect moving objects due to both the irregular movements and deformations of the pixels. In this study, we propose a fast method to detect moving objects in turbulence-degraded image sequences. It combines an efficient registration and background subtraction techniques. Since we model the image degradation as local linear deformations, it is estimated by the motion patterns calculated by optical flow. We utilize feature based optical flow and incremental reference frame generation in registration stage. After warping the frames using the registration result GMM based background subtraction technique detects moving objects in stabilized frames. The experiments performed on common image sequences show that the proposed method detects moving objects faster than the available methods, without distorting the objects.

Anahtar Kelimeler

moving object detection, turbulence degraded video; background subtraction

Kaynakça

• M. Shimizu, S. Yoshimura, M. Tanaka, and M. Okutomi, “Super-Resolution from Image Sequence under Influence of Hot-Air Optical Turbulence,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2008.
• X. Zhu and P. Milanfar, “Removing atmospheric turbulence via space-invariant deconvolution,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. PP, no. 99, pp. 1, 2012.
• X. Zhu and P. Milanfar, “Image Reconstruction from Videos Distorted by Atmospheric Turbulence,” in SPIE, 2010.
• M. Aubailly, M.A. Vorontsov, G.W. Carhat, and M.T. Valley, “Automated Video Enhancement from a Stream of Atmospherically-Distorted Images: The Lucky-Region Fusion Approach,” Proc. SPIE, vol. 7463, 2009.
• M. Hirsch, S. Sra, B. Scho¨lkopf, and S. Harmeling, “Efficient Filter Flow for Space-Variant Multiframe Blind Deconvolution,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 607-614, June 2010.
• R. Cucchiara, C. Grana, M. Piccardi, and A. Prati, “Detecting moving objects, ghosts and shadows in video streams”, IEEE Trans. on Patt. Anal. and Machine Intell., 2003.
• C. Stauffer and W. E. L Grimson, “Adaptive background mixture models for real time tracking,” in CVPR, 1999.
• O. Oreifej, Xin Li, and M. Shah, “Simultaneous video stabilization and moving object detection in turbulence,” in IEEE Trans. On Patt. Anal. and Machine Intell., 2012.
• A. Deshmukh, S. Medasani, andG. Reddy, “Moving Object Detection from Images Distorted by Atmospheric Turbulence,” in ISSP, 2013.
• T. Brox, and J.Malik 2011. Large displacement optical flow: Descriptor matching in variational motion estimation. IEEE Trans. on Pattern Analysis and Machine Intelligence 33, 3 (Mar.), 500–513.
• E. Rosten and T. Drummond, “Machine learning for high speed corner detection,” in 9th Euproean Conference on Computer Vision, vol. 1, pp. 430–443. 2006.
• T. Caliskan, N. Arica, " Atmospheric Turbulence Mitigation Using Optical Flow", ICPR , pp:883-888, 2014.