Estimation of trajectories for accelerated motion from time-varying imagery

This paper is concerned with the estimation of trajectories for accelerated motion from image sequences. Unlike in many other approaches, that assume linear trajectories, we propose a quadratic model that incorporates both velocity and acceleration. This model corresponds better to practical applications especially when the estimation is performed over several images, e.g., in motion-compensated processing with extended temporal support. This is due to the fact that over longer time frame and in the presence of acceleration, quadratic trajectory is capable of providing a better intensity match than a simple displacement. The algorithm for the estimation of dense accelerated motion fields is formulated in this paper using regularization and the solution is based on deterministic relaxation implemented over a pyramid of resolutions. Extensive experimental results for test images with synthetic motion are presented