Randomized algorithm for estimation of moving point position using single camera

Stochastic approximation algorithms (for example SPSA) provide a way to solve optimization problems in the presence of arbitrary but bounded disturbances. In this paper a problem of position estimation for a moving point using monocular projective observations is considered. We add random perturbations to camera position to produce an algorithm which makes estimates of point position demanding only that the point´s velocity is bounded in time. This is superior to the methods currently available in the computer vision field which all consider very restricted cases of point movement (constant, movement in plane). We prove theoretical convergence of estimates and provide numerical simulation for the algorithm.