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Learning Pre-attentive Driving Behaviour from Holistic Visual Features

  • Nicolas Pugeault
  • Richard Bowden
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6316)

Abstract

The aim of this paper is to learn driving behaviour by associating the actions recorded from a human driver with pre-attentive visual input, implemented using holistic image features (GIST). All images are labelled according to a number of driving–relevant contextual classes (eg, road type, junction) and the driver’s actions (eg, braking, accelerating, steering) are recorded. The association between visual context and the driving data is learnt by Boosting decision stumps, that serve as input dimension selectors. Moreover, we propose a novel formulation of GIST features that lead to an improved performance for action prediction. The areas of the visual scenes that contribute to activation or inhibition of the predictors is shown by drawing activation maps for all learnt actions. We show good performance not only for detecting driving–relevant contextual labels, but also for predicting the driver’s actions. The classifier’s false positives and the associated activation maps can be used to focus attention and further learning on the uncommon and difficult situations.

Keywords

Visual Scene Weak Learner Action Prediction Visual Context Human Driver 
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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References

  1. 1.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. International Journal of Computer Vision 42, 145–175 (2001)zbMATHCrossRefGoogle Scholar
  2. 2.
    Torralba, A.: Contextual priming for object detection. International Journal of Computer Vision 53, 169–191 (2003)CrossRefGoogle Scholar
  3. 3.
    Siagian, C., Itti, L.: Rapid biologically-inspired scene classification using features shared with visual attention. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 300–312 (2007)CrossRefGoogle Scholar
  4. 4.
    Douze, M., Jégou, H., Sandhwalia, H., Amsaleg, L., Schmid, C.: Evaluation of gist descriptors for web-scale image search. In: CIVR 2009: Proceedings of the ACM International Conference on Image and Video Retrieval (2009)Google Scholar
  5. 5.
    Torralba, A., Oliva, A., Castelhano, M., Henderson, J.: Contextual guidance of attention in natural scenes: The role of global features on object search. Psychological Review 113, 766–786 (2006)CrossRefGoogle Scholar
  6. 6.
    Renninger, L., Malik, J.: When is scene identification just texture recognition? Vision Research 44, 2301–2311 (2004)Google Scholar
  7. 7.
    Siagian, C., Itti, L.: Biologically inspired mobile robot vision localization. IEEE Transactions on Robotics 25, 861–873 (2009)CrossRefGoogle Scholar
  8. 8.
    Ackerman, C., Itti, L.: Robot steering with spectral image information. IEEE Transactions in Robotics 21, 247–251 (2005)CrossRefGoogle Scholar
  9. 9.
    Kastner, R., Schneider, F., Michalke, T., Fritsch, J., Goerick, C.: Image–based classification of driving scenes by a hierarchical principal component classification (HPCC). In: IEEE Intelligent Vehicles Symposium, pp. 341–346 (2009)Google Scholar
  10. 10.
    Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55, 119–139 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Viola, P., Jones, M.: Robust real–time object detection. International Journal of Computer Vision 57, 137–154 (2001)CrossRefGoogle Scholar
  12. 12.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. The Annals of Statistics 28, 337–407 (2000)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Nicolas Pugeault
    • 1
  • Richard Bowden
    • 1
  1. 1.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyUK

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