**Joon Woong Lee et al. 1995
:** Robust and effective real-time
visual tracking is realized by combining the first order differential
invariants with stochastic filtering. The Kalman filter as an optimal
stochastic filter is used to estimate the motion parameters, namely 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 deformation, we compute the first 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 differential invariants. We demonstrate the robustness and feasibility of
the proposed tracking algorithm through real experiments in which an X-Y
Cartesian robot tracks a toy vehicle moving along 3D
rails.