Ceiling Analysis of Pedestrian Recognition Pipeline for an Autonomous Car Application

This paper presents an exploration of the ceiling analysis of machine learning systems. It also provides an approach to the development of pedestrian recognition systems using this analysis. A pedestrian detection pipeline is simulated in order to evaluate this method. The advantage of this method is that it allows determining the most promising pipeline's elements to be modified as a way of more efficiently improving the recognition system. The pedestrian recognition is based on computer vision and is intended for an autonomous car application. A Linear SVM used as classifier enables the recognition, so this development is also addressed as a machine learning problem. This analysis concludes that for this application the more worthy path to be followed is the improvement of the pre-processing method instead of the classifier.