A genetic algorithm for optical flow estimation
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摘要
This paper illustrates a new optical flow estimation technique that builds upon a genetic algorithm (GA). First, the current frame is segmented into generic shape regions, using only luminance and color 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 initialize 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 with a multi-resolution version of the well-known Lucas–Kanade differential algorithm. Our simulations demonstrate that, with respect to Lucas–Kanade, it significantly reduces the energy of the motion-compensated residual error.
论文关键词:Optical flow,Genetic algorithms,Motion estimation
论文评审过程:Received 20 April 2004, Revised 4 June 2005, Accepted 31 January 2006, Available online 7 July 2006.
论文官网地址:https://doi.org/10.1016/j.imavis.2006.01.021