Application of Random Ferns for non-planar object detection

Abstract

The real time object detection task is considered as a part of a project devoted to development of autonomous ground robot. This problem has been successfully solved with Random Ferns algorithm, which belongs to keypoint-based method and uses fast machine learning algorithms for keypoint matching step. As objects in the real world are not always planar, in this article we describe experiments of applying this algorithm for non-planar objects. Also we introduce a method for fast detection of a special class of non-planar objects | those which can be decomposed into planar parts (e.g. faces of a box). This decomposition needs one detector for each side, which may significantly affect speed of detection. Proposed approach copes with it by omitting repeated steps for each detector and organizing special queue of detectors. It makes the algorithm three times faster than naive one.