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: Incremental Learning for Motion Prediction of Pedestrians and Vehicles
  name:
    name part: Govea, Alejandro Dizan Vasquez.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2010
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: 160 p. 35 illus. in color. online resource.
  abstract: Modeling and predicting human and vehicle motion is an active research domain. Owing to the difficulty in modeling the various factors that determine motion (e.g. internal state, perception) this is often tackled by applying machine learning techniques to build a statistical model, using as input a collection of trajectories gathered through a sensor (e.g. camera, laser scanner), and then using that model to predict further motion. Unfortunately, most current techniques use offline learning algorithms, meaning that they are not able to learn new motion patterns once the learning stage has finished. This books presents a lifelong learning approach where motion patterns can be learned incrementally, and in parallel with prediction. The approach is based on a novel extension to hidden Markov models, and the main contribution presented in this book, called growing hidden Markov models, which gives us the ability to learn incrementally both the parameters and the structure of the model. The proposed approach has been extensively validated with synthetic and real trajectory data. In our experiments our approach consistently learned motion models that were more compact and accurate than those produced by two other state-of-the-art techniques, confirming the viability of lifelong learning approaches to build human behavior models.
  table of contents: I: Background -- Probabilistic Models -- II: State of the Art -- Intentional Motion Prediction -- Hidden Markov Models -- III: Proposed Approach -- Growing Hidden Markov Models -- Learning and Predicting Motion with GHMMs -- IV: Experiments -- Experimental Data -- Experimental Results -- V: Conclusion -- Conclusions and Future Work.
  note: by Alejandro Dizan Vasquez Govea.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Pattern recognition
  subject:
    topic: Robotics
  subject:
    topic: Automation
  subject:
    topic: Engineering
  subject:
    topic: Robotics and Automation
  subject:
    topic: Pattern Recognition
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  classification: TJ210.2-211.495
  classification: T59.5
  classification: 629.892
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Springer Tracts in Advanced Robotics, 64
  identifier: 9783642136429
  identifier: http://dx.doi.org/10.1007/978-3-642-13642-9
  record info:
    record creation date: 100715
    record change date: 20151204152452.0
    record identifier: 978-3-642-13642-9