Abstract
We propose a segment based moving edge detection algorithm by building association from multi-frames of the scene. A statistical background model is used to segregate the moving segments that utilize shape and position information. Edge specific knowledge depending upon background environment is computed and thresholds are determined automatically. Statistical background model gives flexibility for matching background edges. Building association within the moving segments of multi-frame enhances the detection procedure by suppressing noisy detection of flickering segments that occurs frequently due to noise, illumination variation and reflectance in the scene. The representation of edge as edge segment allows us to incorporate this knowledge about the background environment. Experiments with noisy images under varying illumination changing situation demonstrates the robustness of the proposed method in comparison with existing edge pixel based moving object detection methods.
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References
Ahn, Y., Ahn, K., Chae, O.: Detection of moving objects edges to implement home security system in a wireless environment. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds.) ICCSA 2004. LNCS, vol. 3043, pp. 1044–1051. Springer, Heidelberg (2004)
Bebis, G., Georgiopoulos, M., Lobo, N.D.V., Shah, M., Bebis, D.G.: Learning affine transformations. Pattern Recognition 32, 1783–1799 (1999)
Borgefors, G.: Hierarchical chamfer matching: a parametric edge matching algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence 10(6), 849–865 (1988)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)
Dailey, D.J., Cathey, F.W., Pumrin, S.: An algorithm to estimate mean traffic speed using uncalibrated cameras. IEEE Transactions on Intelligent Transportation Systems 1(2), 98–107 (2000)
Dewan, M.A.A., Hossain, M.J., Chae, O.: Background independent moving object segmentation for video surveillance. IEICE Transactions 92-B(2), 585–598 (2009)
Fitzgibbon, A.W.: Robust registration of 2d and 3d point sets. Image and Vision Computing 21(13-14), 1145–1153 (2003); british Machine Vision Computing 2001
Hossain, M.J., Dewan, M.A.A., Chae, O.: Moving Object Detection for Real Time Video Surveillance: An Edge Based Approach. IEICE Trans. Commun. E90-B(12), 3654–3664 (2007)
Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Transactions on Systems, Man and Cybernetics 34, 334–352 (2004)
Kim, C., Hwang, J.N.: Fast and automatic video object segmentation and tracking for content-based applications. IEEE Trans. Circuits Syst. Video Techn. 12(2), 122–129 (2002)
Makarov, A., Vesin, J.-M., Kunt, M.: Intrusion detection using extraction of moving edges. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition, 1994. Conference A: Computer Vision and Image Processing, vol. 1, pp. 804–807 (October 1994)
Radke, R.J., Andra, S., Al-Kofahi, O., Roysam, B.: Image change detection algorithms: A systematic survey. IEEE Transactions on Image Processing 14, 294–307 (2005)
Roh, M.C., Kim, T.Y., Park, J., Lee, S.W.: Accurate object contour tracking based on boundary edge selection. Pattern Recognition 40(3), 931–943 (2007)
Sebastian, T.B., Klein, P.N., Kimia, B.B.: On aligning curves. IEEE Trans. Pattern Anal. Mach. Intell. 25(1), 116–125 (2003)
Turney, J.L., Mudge, T.N., Volz, R.A.: Recognizing partially occluded parts. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI 7(4), 410–421 (1985)
Vincent, L., Soille, P.: Watersheds om digital spaces: An efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(6), 583–598 (1991)
Wolfson, H.J.: On curve matching. IEEE Trans. Pattern Anal. Mach. Intell. 12(5), 483–489 (1990)
Yi, X., Camps, O.I.: Line-based recognition using a multidimensional hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 901–916 (1999)
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Murshed, M., Ramirez, A., Chae, O. (2011). Moving Edge Segment Matching for the Detection of Moving Object. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21593-3_28
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DOI: https://doi.org/10.1007/978-3-642-21593-3_28
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