Video Tracking Association Problem Using Estimation of Distribution Algorithms in Complex Scenes

In this work an efficient and robust technique of data association will be developed as a search in the hypotheses space defined by the possible association between detections and tracks. The full data association problem in visual tracking is formulated as a hypotheses search with a heuristic evaluation function to take into account structural and specific information such as distance, shape, colour, etc. This heuristic should represent the real problem so that its optimization leads to the solution of each situation. In order to guarantee performance in real time, the search process will have assigned a bounded amount of time to give the solution. The number of evaluations is restricted to accomplish this bound. The use of Estimation Distribution Algorithms (EDA) allows the application of an Evolutionary Computation technique to search in the hypothesis space. The performance of alternative algorithms used to provide the solution with this time constraint will be compared considering complex situations.

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