Task and Context aware Performance Evaluation of Computer Vision Algorithms

Abstract

Developing a robust computer vision algorithm is very difficult because of the enormous variation of visual conditions. A systems technology solution to this challenge is an automatic selection and configuration of different existing algorithms according to the task and context of arbitrary applications. This paper presents a first attempt to generate the required mapping between the task/context to the optimal algorithm and algorithm configuration. This mapping is based on an extensive performance evaluation. To practically handle the exhaustive search for optimal solutions a new optimization challenge the Multiple-Multi Objective Optimization (M-MOP) and an according solution based on genetic algorithms is developed and evaluated. The results show the robustness of the approach and guide further development towards an automatic vision system generation.