A genetic algorithm for optical flow estimation
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 LucasKanade differential algorithm. Our simulations demonstrate that, with respect to LucasKanade, it significantly reduces the energy of the motion-compensated residual error.
genetic algorithms optical flow motion estimation