Comparison of Shadow Detection based on HSV and YCbCr Color Space

Aye Aye Win

Abstract


The shadow detection of moving vehicle is a prominent task for all system of vision by computer. Therefore, this study is analyzed the image pixel values of shadows and vehicles based on HSV and YCbCr color spaces and is compared these two color models for getting higher shadow detection rate. The HSV and YCbCr color spaces are evaluated by Thresholding Method using the MATLAB programming. The foreground and background objects are detected by using HSV and YCbCr Color Space. According to the result, The HSV color Space is detected shadows more effectively than YCbCr even though applying auto Thresholding Method in both color spaces.


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References


T. Bouwmans, “Recent advanced statistical background modeling for foreground detection: A systematic survey,” Recent Patents on Computer Science, vol. 4, no. 3, pp. 147-171, 2011.

C. Stauffer and W. E. L. Grimson, “Adaptive background mixture models for real-time tracking,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '99), Fort Collins, Colo, USA, vol. 2, pp. 246–252, 1999.

S. J. McKenna, et al., “Tracking groups of people,” Computer vision and image understanding, vol. 80, no. 1, pp. 20-56, 2000.

K. Kim, et al., “Real-time foreground background segmentation using codebook model,” Real-Time Imaging, vol. 11, no. 3, pp. 172-185, 2005.

R. S. Sabeenian and S. Lavanya, “High de_nition video segmentation techniques: A review,” International Journal of Computer and Electrical Engineering, vol. 5, no. 6, pp. 559-562, 2013.

M. Xu and T. Ellis, “Illumination-invariant motion detection using color mixture models,” British Machine Vision Conf (BMVA 2001), Manchester, pp. 163-172, 2001.

M. Harville, et al., “Foreground segmentation using adaptive mixture models in color and depth,” Proc of the IEEE Workshop on Detection and Recognition of Events in Video, Vancouver, Canada, pp. 3-11, 2001.

Y. Sun, et al., “Better foreground segmentation for static cameras via new energy form and dynamic graph-cut,” 18th Int Conf on Pattern Recognition (ICPR 2006), pp. 49-52, 2006.

S. Yang and C. Hsu, “Background modeling from GMM likelihood combined with spatial and color coherency,” ICIP, Atlanta, USA, pp. 2801-2804, 2006.

P. KaewTraKulPong and R. Bowden, “An improved adaptive background mixture model for real time tracking with shadow detection,” In Proceedings of the 2nd European Workshop on Advanced Video Based Surveillance Systems (AVBS'01), pp.1-5, 2001.

R. Cucchiara, et al., “Detecting objects, shadows and ghosts in video streams by exploiting color and motion information,” Proceedings of the 11th International Conference on Image Analysis and Processing (ICIAP 2001), Palermo, Italy, pp. 360-365, 2001.

R. Cucchiara, et al., “The sakbot system for moving object detection and tracking,” video based surveillance systems: Computer vision and distributed processing, pp. 145-158, 2001.


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