Real Time Image Analysis for Infomobility

  • Massimo Magrini
  • Davide Moroni
  • Gabriele Pieri
  • Ovidio Salvetti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7252)


In our society, the increasing number of information sources is still to be fully exploited for a global improvement in urban living. Among these, a big role is played by images and multimedia data (i.e. coming from CCTV and surveillance videos, traffic cameras, etc.). This along with the wide availability of embedded sensor platforms and low-cost cameras makes it now possible the conception of pervasive intelligent systems based on vision. Such systems may be understood as distributed and collaborative sensor networks, able to produce, aggregate and process images in order to understand the observed scene and communicate the relevant information found about it. In this paper, we investigate the characteristics of image processing algorithms coupled to visual sensor networks. In particular the aim is to define strategies to accomplish the tasks of image processing and analysis over these systems which have rather strong constraints in computational power and data transmission. Thus, such embedded platform cannot use advanced computer vision and pattern recognition methods, which are power consuming, on the other hand, the platform may be able to exploit a multi-node strategy that allows to perform a hierarchical processing, in order to decompose a complex task into simpler problems. In order to apply and test the described methods, a solution to a visual sensor network for infomobility is proposed. The experimental setting considered is two-fold: acquisition and integration of different views of parking lots, and acquisition and processing of traffic-flow images, in order to provide a complete description of a parking scenario and its surrounding area.


False Alarm Rate Scale Invariant Feature Transform Camera Node Change Detection Algorithm Scale Invariant Feature Transform Descriptor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Massimo Magrini
    • 1
  • Davide Moroni
    • 1
  • Gabriele Pieri
    • 1
  • Ovidio Salvetti
    • 1
  1. 1.Institute of Information Science and Technologies (ISTI)National Research Council of Italy (CNR)PisaItaly

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