A Hierarchical Swarm System for Robust Real-Time Feature Detection

In this thesis, we discuss a simple extension to the standard particle swarm optimization algorithm, inspired by genetic algorithms that allow swarms to cope better with dynamically changing fitness evaluations for a given parameter space. We demonstrate the utility of the extension in an application system for dynamical facial feature detection and tracking, which uses the proposed 'real-time evolving swarms' for a continuous dynamic search of the best locations in a two-dimensional parameter space to improve upon feature detection with static parameters. We show in several experimental evaluations that the proposed method is robust to lighting changes and does not require any calibration. Moreover, the method works in real time, is computationally tractable, and not limited to the employed static feature detector, but can be applied to any n-dimensional search space.Further, this thesis introduces a novel hierarchical extension to the standard particle swarm optimization algorithm that allows swarms to cope better with dynamically changing fitness evaluations for a given parameter space. It present the formal framework and demonstrate the utility of the extension in an application system for dynamic face detection. Specifically, the feature detector/tracker uses the proposed 'hierarchical real-time swarms' for a continuous concurrent dynamic search of the best locations in a two-dimensional parameter space and the image space to improve upon feature detection and tracking in changing environments.