Automatic parameter regulation of perceptual systems

Abstract Changes in environmental conditions frequently degrade the performance of perceptual systems. This article proposes a system architecture with a control component that auto-regulates parameters to provide a reduction in the sensitivity to environmental changes. We demonstrate the benefit of this architecture using the example of a long-term tracking system. The control component consists of modules for auto-critical evaluation, for auto-regulation of parameters and for error recovery. Both modules require a measure of the goodness of system output with respect to a scene reference model. We describe the generation of the scene reference model and propose measures for the model quality and for the goodness of system output in form of measurement trajectories. Our self-adaptive tracking system achieves better recall than a manually tuned tracking system on a public benchmark data set.