Abstract
Statistical patch-based observation (SPBO) is built specifically for obtaining good tracking observation in robust environment. In video analytics applications, the problems of blurring, moderate deformation, low ambient illumination, homogenous texture and illumination change are normally encountered as the foreground objects move. We approach the problems by fusing both feature and template based methods. While we believe that feature based matchings are more distinctive, we consider that object matching is best achieved by means of a collection of points as in template based detectors. Our algorithm starts by building comparison vectors at each detected point of interest between consecutive frames. The vectors are matched to build possible patches based on their respective coordination. Patch matching is done statistically by modelling the histograms of patches as Poisson distributions for both RGB and HSV colour models. Then, maximum likelihood is applied for position smoothing while a Bayesian approach is applied for size smoothing. Our algorithm performs better than SIFT and SURF detectors in a majority of the cases especially in complex video scenes.
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Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 679–698 (1986)
Duda, R.O., Hart, P.E.: In: Sobel, I., Feldman, G. (eds.) A 3x3 Isotropic Gradient Operator for Image Processing, pp. 271–272 (1973)
Roberts, L.G.: Machine perception of three-dimensional solids. PhD thesis, Dept. of Electrical Engineering, Massachusetts Institute of Technology (1963)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, pp. 147–151 (1988)
Shi, J., Tomasi, C.: Good features to track. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 593–600 (1994)
Ke, Y., Suthankar, R.: Pca-sift: a more distinctive representation for local image descriptors. In: IEEE Computer Society Conference Computer Vision and Pattern Recognition, vol. 2, pp. 506–513 (2004)
Burghouts, G.J., Geusebroek, J.M.: Performance evaluation of local colour invariants. Computer Vision and Image Understanding 113, 48–62 (2008)
Geusebroek, J.M., Smeulders, A.W.M., Boomgaarda, R.V.D.: Color invariance. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 1338–1350 (2001)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1615–1630 (2005)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Computer Vision and Image Understanding 110, 346–359 (2008)
Kay, S.M.: Fundamentals of Statistical Signal Processing, Detection Theory, vol. 2. Prentice Hall, Englewood Cliffs (1998)
Evans, C.: Notes on the opensurf library. Technical Report CSTR-09-001, University of Bristol (January 2009)
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Zulkifley, M.A., Moran, B. (2011). Statistical Patch-Based Observation for Single Object Tracking. In: Maino, G., Foresti, G.L. (eds) Image Analysis and Processing – ICIAP 2011. ICIAP 2011. Lecture Notes in Computer Science, vol 6979. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24088-1_13
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DOI: https://doi.org/10.1007/978-3-642-24088-1_13
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