Visual motion based behavior learning using hierarchical discriminant regression

  • Authors:

  • Changjiang Yang;Juyang Weng

  • Affiliations:

  • Department of Computer Science and Engineering, Michigan State University, East Lansing, MI;Department of Computer Science and Engineering, Michigan State University, East Lansing, MI

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2002

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Abstract

This paper presents a new technique which incrementally builds a hierarchical discriminant regression (IHDR) tree for generation of motion based robot reactions. The robot learned the desired reactions from motion change images, without using other pre-defined features. The generation from training cases is accomplished through the automatically constructed IHDR tree, which automatically derives features that are most related to outputs and disregards subspaces that are not related, or less related, to outputs. The real-time speed is achieved through combination of feature space partition and a coarse-to-fine sample search, which results in a logarithmic time complexity in the number of nodes. The experiments showed that the IHDR method can interpolate the mapping between high dimensional input space and the output control signal space from a variety of objects of various shapes with different motion patterns, based on the size-dependent negative logarithmic distance measures in the hierarchical feature space. The trained robot is able to aim to its camera towards moving object and move toward or away according to the size of moving object.