David Gerónimo, Antonio M. López

Research output: Chapter in BookChapterResearchpeer-review


© The Author(s) 2014. The classification module receives a list of candidates (rectangular windows in practice) that are likely to contain a pedestrian. In this stage, such candidates are classified as pedestrian or non-pedestrian with the goal of minimizing the number of wrong decisions while maximizing right ones. Broadly speaking, this is a pattern recognition module involving vision and machine learning. The former field dealing with image descriptors and pedestrian models. The later one providing algorithms to learn, mostly from labeled samples, the pedestrian/non-pedestrian decision rule (i.e., the pedestrian classifier) based on the mentioned descriptors and models.
Original languageEnglish
Title of host publicationSpringerBriefs in Computer Science
Number of pages48
ISBN (Electronic)2191-5776
Publication statusPublished - 1 Jan 2014


  • Gradient magnitude
  • Gradient orientation
  • Pedestrian detection
  • Scale invariant feature transform
  • Weak classifier


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