Common KADS Library for Expertise Modelling
Common KADS Library for Expertise Modelling
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Hybridizing Particle Filters and Population-based Metaheuristics for Dynamic Optimization Problems
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Scatter search particle filter for 2d real-time hands and face tracking
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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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.