Girgit: A Dynamically Adaptive Vision System for Scene Understanding

  • Leonardo M. Rocha
  • Sagar Sen
  • Sabine Moisan
  • Jean-Paul Rigault
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6962)


Modern vision systems must run in continually changing contexts. For example, a system to detect vandalism in train stations must function during the day and at night. The vision components for acquisition and detection used during daytime may not be the same as those used at night. The system must adapt to a context by replacing running components such as image acquisition from color to infra-red. This adaptation must be dynamic with detection of context, decision on change in system configuration, followed by the seamless execution of the new configuration. All this must occur while minimizing the impact of dynamic change on validity of detection and loss in performance. We present Girgit, a context-aware vision system for scene understanding, that dynamically orchestrates a set of components. A component encapsulates a vision-related algorithm such as from the OpenCV library. Girgit inherently provides loading/caching of multiple component instances, system reconfiguration, management of incoming events to suggest actions such as component re-configuration and replacement of components in pipelines. Given the surplus architectural layer for dynamic adaptation one may ask, does Girgit degrade scene understanding performance? We perform several empirical evaluations on Girgit using metrics such as frame-rate and adaptation time to answer this question. For instance, the average adaptation time between change in configurations is less than 2 μs with caching, while 8 ms without caching. This in-turn has negligible effect on scene understanding performance with respect to static C++ implementations for most practical purposes.


Vision System Frame Rate Intrusion Detection Face Detection Software Product Line 
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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  1. 1.
  2. 2.
  3. 3.
    Hameurlain, C.B.N., Barbier, F.: Mocas: a model-based approach for building self-adaptive software components. In: ECMDA (2009)Google Scholar
  4. 4.
    Eugster, P., Felber, P.A., Guerraoui, R., Kermarrec, A.M.: The many faces of publish/subscribe. ACM Computing Surveys 35, 114–131 (2003)CrossRefGoogle Scholar
  5. 5.
    Garlan, D., Cheng, S.W., Huang, A.C., Schmerl, B., Steenkiste, P.: Rainbow: architecture-based self-adaptation with reusable infrastructure. Computer 37(10), 46–54 (2004)CrossRefGoogle Scholar
  6. 6.
  7. 7.
    KaewTrakulPong, P., Bowden, R.: A real time adaptive visual surveillance system for tracking low-resolution colour targets in dynamically changing scenes. Image and Vision Computing 21(10), 913–929 (2003), CrossRefGoogle Scholar
  8. 8.
    Kattepur, A., Sen, S., Baudry, B., Benveniste, A., Jard, C.: Variability modeling and qos analysis of web services orchestrations. In: Proceedings of the 2010 IEEE International Conference on Web Services, ICWS 2010, pp. 99–106. IEEE Computer Society, Washington, DC, USA (2010), CrossRefGoogle Scholar
  9. 9.
    Morin, B., Barais, O., Jezequel, J.-M., Fleurey, F., Solberg, A.: Models@ run.time to support dynamic adaptation. Computer 42(10), 44–51 (2009)CrossRefGoogle Scholar
  10. 10.
    Perrouin, G., Sen, S., Klein, J., Baudry, B., Le Traon, Y.: Automatic and scalable t-wise test case generation strategies for software product lines. In: International Conference on Software Testing (ICST). IEEE, Paris (2010), Google Scholar
  11. 11.
    Zhang, C.: Model-based development of dynamically adaptive software. In: ICSE 2006 Proceedings of the 28th International Conference on Software Engineering. ACM, New York (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Leonardo M. Rocha
    • 1
  • Sagar Sen
    • 1
  • Sabine Moisan
    • 1
  • Jean-Paul Rigault
    • 1
  1. 1.INRIA, Sophia-AntipolisSophia-AntipolisFrance

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