A Kalman filter based visual tracking algorithm for an object moving in 3D

Robust and effective real-time visual tracking is realized by combining the first order differential invariants with the stochastic filtering. The Kaltnan filler as an optimal stochastic filter is used to estimate the motion parameters, narnely the plant state vector of the moving object with the unknown dynamics in successive image frames. Using the fact that the relative motion between the moving object and the moving observer causes the image deformation, we compute the first order differential invariants of the image velocity field. The surface orientation and the depth estimate between the observer and the object are computed based on these first order dfferential invariants. We demonstrate the robustness and feasibility of the proposed tracking algorithm through real experiments in which an X-Y Cartesian robot tracks a t0.y vehicle nioving along 3 0 rails.