Automatic parameter regulation for a tracking system with an auto-critical function

In this article we propose an architecture of a tracking system that can judge its own performance by an auto-critical function. Performance drops can be detected which trigger an automatic parameter regulation module. This regulation module is an expert system that searches a parameter setting with better performance and returns it to the tracking system. With such an architecture, a robust tracking system can be implemented which automatically adapts its parameters in case of changes in the environmental conditions. This article opens a way to self-adaptive systems in detection and recognition.