Two computer vision-based tracking applications solved using a robust parallel optimizer

A method was developed as a response to the need for a robust optimizer for two different image vision algorithms. Our new approach is a synthesis of two modalities, a simulated annealing technique paired with a parallel search. Using an initial value and the maximum parametric variations, our method searches for a cost function minimum by using parameter subspaces combined with a local simulated annealing algorithm step. The description of our two computer vision applications which relate object tracking to video sequences make it clear why this kind of optimizer was needed. In the cases described, our method provides encouraging results.