Optical flow estimation is a widely known problem in computer vision
introduced by Gibson, J.J(1950) to describe the visual perception of human by
stimulus objects. Estimation of optical flow model can be achieved by solving
for the motion vectors from region of interest in the the different timeline...
In this paper, we assumed slightly uniform change of velocity between two
nearby frames, and solve the optical flow problem by traditional method,
Lucas-Kanade(1981). This method performs minimization of errors between
template and target frame warped back onto the template. Solving minimization
steps requires optimization methods which have diverse convergence rate and
error. We explored first and second order optimization methods, and compare
their results with Gauss-Newton method in Lucas-Kanade. We generated 105 videos
with 10,500 frames by synthetic objects, and 10 videos with 1,000 frames from
real world footage. Our experimental results could be used as tuning parameters
for Lucas-Kanade method.