In this paper, we introduce a 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. We 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. We show in several experimental evaluations on a robot interacting with people in real-time that the proposed method is robust to lighting changes and does not require any calibration. Moreover, the method is not limited to face detection, but can be applied to any n-dimensional search space
['Matthias Scheutz']
Real-Time Hierarchical Swarms for Rapid Adaptive Multi-Level Pattern Detection and Tracking