rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Robust Motion Detection in Real-Life Scenarios
  name:
    name part: Martínez-Martín, Ester.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Pobil, Ángel P. del.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxk
    date issued: 2012
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XII, 108 p. 70 illus., 61 illus. in color. online resource.
  abstract: Our knowledge of the surrounding world is obtained by our senses, of which vision is the most important for the information it can provide. In artificial systems, the field of Computer Vision aims to identify physical objects and scenes from captured images, to make useful decisions. This involves the processing and analysis of images, video data, and multi-dimensional data like medical scans. In this context, motion provides a stimulus for detecting objects in movement within the observed scene. Moreover, motion allows other characteristics to be obtained, such as object shape, speed or trajectory, which are meaningful for detection and recognition. However, the motion observable in a visual input can be due to different factors: movement of the imaged objects, movement of the observer, motion of the light sources, or a combination of these. This work focuses on motion detection from images captured by perspective and fisheye still cameras, proposing a complete sensor-independent visual system that provides robust target motion detection. First, the way sensors obtain images is studied, allowing a spatial analysis of motion to be carried out. Then, a novel background maintenance approach for robust target motion detection is implemented. Two different situations are considered: a fixed camera observing a constant background where objects are moving; and a still camera observing objects in movement against a dynamic background. This permits the development of a surveillance mechanism that removes the constraint of observing a scene free of foreground elements to obtain a reliable background model, since this situation cannot be guaranteed when operating in an unknown environment. Other problems are also addressed for the successful handling of changes in illumination, the distinction between foreground and background elements, and non-uniform vacillating backgrounds.
  table of contents: Introduction -- Motion Detection in Static Backgrounds -- Motion Detection in General Backgrounds -- Applications -- Computer Vision Terms.
  note: by Ester Martínez-Martín, Ángel P. del Pobil.
  subject:
    topic: Computer science
  subject:
    topic: Image processing
  subject:
    topic: Computer Science
  subject:
    topic: Image Processing and Computer Vision
  classification: TA1637-1638
  classification: TA1634
  classification: 006.6
  classification: 006.37
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: SpringerBriefs in Computer Science
  identifier: 9781447142164
  identifier: http://dx.doi.org/10.1007/978-1-4471-4216-4
  record info:
    record creation date: 120710
    record change date: 20151204153211.0
    record identifier: 978-1-4471-4216-4