Solving video-association problem with explicit evaluation of hypothesis using EDAs

In this work the data association problem in visual tracking is formulated as a combinatorial hypotheses search with a heuristic evaluation function taking into account structural and specific information such as distance, shape, colour, etc. In order to guarantee real time performance, the search process has a time limit to explore alternative solutions. This time defines the upper bound of the number of evaluations depending on the efficiency of the search algorithm. Estimation distribution algorithms (EDA) is proposed as an efficient evolutionary computation technique to search in this hypothesis space. Then, an exhaustive comparison of the performance of alternative algorithms is carried out considering complex representative situations in real video sets.