Sensor Data Understanding

Marcin Grzegorzek

Cite this publication as

Marcin Grzegorzek, Sensor Data Understanding (2017), Logos Verlag, Berlin, ISBN: 9783832592455

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Description / Abstract

The rapid development in the area of sensor technology has been responsible for a number of societal phenomena like UGC (User Generated Content) or QS (Quantified Self). Machine learning algorithms benefit a lot from the availability of such huge volumes of digital data. For example, new technical solutions for challenges caused by the demographic change (ageing society) can be proposed in this way, especially in the context of healthcare systems in industrialised countries. The goal of this book is to present selected algorithms for Visual Scene Analysis (VSA, processing UGC) as well as for Human Data Interpretation (HDI, using data produced within the QS movement) and to expose a joint methodological basis between these two scientific directions. While VSA approaches have reached impressive robustness towards human-like interpretation of visual sensor data, HDI methods are still of limited semantic abstraction power. Using selected state-of-the-art examples, this book shows the maturity of approaches towards closing the semantic gap in both areas, VSA and HDI.

Table of content

  • BEGINN
  • Preface
  • I Introduction
  • Fundamental Concept
  • II Visual Scene Analysis
  • Large-Scale Multimedia Retrieval
  • Shape-Based Object Recognition
  • Moving Object Analysis for Video Interpretation
  • III Human Data Interpretation
  • Physical Activity Recognition
  • Cognitive Activity Recognition
  • Emotion Recognition
  • IV Conclusion
  • Summary and Future Vision
  • List of Figures
  • List of Tables

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