On knowledge modelling of the Visual Tracking task

摘要

This paper describes the knowledge modelling for a complex object tracking system based on a video sequence. By complex object tracking problems, we mean those based on articulated models and also the possibility of tracking multiple targets. The general proposed framework to solve the considered problem, referred as the “Visual Tracking” task, is based on a synergic combination of strategies coming from particle filters and population-based metaheuristics.The considered system works with a population of individuals (solutions) that evolve along time. These individuals cooperate among them and also improve their respective fitness values in order to offer an efficient near-optimal solution for each frame in the tracked video sequence. The three main resulting subtasks, namely “Extract”, “Explore” and “Exploit‘”, are described in detail by CommonKADS library schemes. We argue that the knowledge modelling components used in the decomposition of the Visual Tracking task can be reused as generic elements of problem-solving methods in video processing.