Abstract
This paper illustrates a new optical flow estimation technique, which builds upon a genetic algorithm (GA). First, the current frame is segmented into generic shape regions, using only brightness information. For each region a two-parameter motion model is estimated using a GA. The fittest individuals identified at the end of this step are used to initialise the population of the second step of the algorithm, which estimates a six-parameter affine motion model, again using a GA. The proposed method is compared against a multiresolution version of the well-known Lukas-Kanade differential algorithm. It proved to yield the same or better results in term of energy of the residual error, yet providing a compact representation of the optical flow, making it particularly suitable to video coding applications.
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© 2006 Springer-Verlag Berlin Heidelberg
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Tagliasacchi, M. (2006). Optical Flow Estimation Using Genetic Algorithms. In: Di Gesú, V., Masulli, F., Petrosino, A. (eds) Fuzzy Logic and Applications. WILF 2003. Lecture Notes in Computer Science(), vol 2955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10983652_37
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DOI: https://doi.org/10.1007/10983652_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31019-8
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