Classifier and Feature Based Stereo for Mobile Robot Systems

Classifier based approaches to stereo vision reduce the ambiguity associated with low level texture and feature based image registration, however there are challenges associated with providing accurate object positioning for good depth estimation using these high level approaches. This paper investigates the performance of stereo based systems that use Haar-like features for object classification. The availability of good face detectors using this approach makes it suitable for biped and mobile robot systems that operate in environments that include people, however significant challenges exist for identifying general objects that are not as highly structured and aligned as human faces.