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Self-aware Object Tracking in Multi-Camera Networks

  • Lukas Esterle
  • Jennifer Simonjan
  • Georg Nebehay
  • Roman Pflugfelder
  • Gustavo Fernández Domínguez
  • Bernhard Rinner
Chapter
Part of the Natural Computing Series book series (NCS)

Abstract

This chapter discusses another example of self-aware and self-expressive systems: a multi-camera network for object tracking. It provides a detailed description of how the concepts of self-awareness and self-expression can be implemented in a real network of smart cameras. In contrast to traditional cameras, smart cameras are able to perform image analysis on-board and collaborate with other cameras in order to analyse the dynamic behaviour of objects in partly unknown environments. Self-aware and self-expressive smart cameras are even able to reason about their current state and to adapt their algorithms in response to changes in their environment and the network. Self-awareness and self-expression allow them to manage the trade-off among performance, flexibility, resources and reliability during runtime. Due to the uncertainties and dynamics in the network a fixed configuration of the cameras is infeasible. We adopt the concepts of self-awareness and self-expression for autonomous monitoring of the state and progress of each camera in the network and adapt its behaviour to changing conditions. In this chapter we focus on describing the building blocks for self-aware camera networks and demonstrate the key characteristics in a multi-camera object tracking application both in simulation and in a real camera network. The proposed application implements the goal sharing with time-awareness capability pattern, including meta-self-awareness capabilities as discussed in Chapter 5. Furthermore, the distributed camera network employs the middleware system described in Chapter 11 to facilitate distributed coordination of tracking responsibilities. Moreover, the application uses socially inspired techniques and mechanisms discussed in Chapter 7.

Keywords

Object Tracking Foreground Object Camera Network Handover Strategy Tracking Responsibility 
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 International Publishing Switzerland 2016

Authors and Affiliations

  • Lukas Esterle
    • 1
  • Jennifer Simonjan
    • 1
  • Georg Nebehay
    • 2
  • Roman Pflugfelder
    • 2
  • Gustavo Fernández Domínguez
    • 2
  • Bernhard Rinner
    • 2
  1. 1.Alpen-Adria-Universität KlagenfurtKlagenfurtAustria
  2. 2.Austrian Institute of TechnologySeibersdorfAustria

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