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Learning Object Detectors in Stationary Environments

  • Chapter
Book cover Advanced Topics in Computer Vision

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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Abstract

The most successful approach for object detection is still applying a sliding window technique, where a pre-trained classifier is evaluated on different locations and scales. In this chapter, we interrogate this strategy in the context of stationary environments. In particular, having a fixed camera position observing the same scene a lot of prior (spatio-temporal) information is available. Exploiting this specific scene information allows for (a) improving the detection performance and (b) for reducing the model complexity; both on reduced computational costs! These benefits are demonstrated for two different real-world tasks (i.e., person and car detection). In particular, we apply two different evaluation/update strategies (holistic, grid-based), where any suited online learner can be applied. In our case we demonstrate the proposed approaches for different applications and scenarios, clearly showing their benefits compared to generic methods.

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Notes

  1. 1.

    We refer a classifier to as an oracle, if it has a high precision, even at a low recall, and can thus be used to generate new training samples.

  2. 2.

    http://www.cvg.rdg.ac.uk/PETS2006/ (November 30, 2012).

  3. 3.

    http://www.eecs.qmul.ac.uk/~andrea/avss2007_d.html (November 30, 2012).

  4. 4.

    This particular task was chosen as implementations of existing approaches as well as a number of benchmark datasets are publicly available.

  5. 5.

    http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1 (November 30, 2012).

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Acknowledgements

The work was supported by the Austrian Science Foundation (FWF) project Advanced Learning for Tracking and Detection in Medical Workflow Analysis (I535-N23) and by the Austrian Research Promotion Agency (FFG) projects SHARE (831717) in the IV2Splus program and MobiTrick (8258408) in the FIT-IT program.

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Authors and Affiliations

  1. Institute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria

    Peter M. Roth, Sabine Sternig & Horst Bischof

Authors
  1. Peter M. Roth
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  2. Sabine Sternig
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  3. Horst Bischof
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Corresponding author

Correspondence to Peter M. Roth .

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Editors and Affiliations

  1. Dipartimento di Matematica e Informatica, Università di Catania, Catania, Italy

    Giovanni Maria Farinella

  2. Dipartimento di Matematica e Informatica, Università di Catania, Catania, Italy

    Sebastiano Battiato

  3. Department of Engineering, University of Cambridge, Cambridge, United Kingdom

    Roberto Cipolla

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Roth, P.M., Sternig, S., Bischof, H. (2013). Learning Object Detectors in Stationary Environments. In: Farinella, G., Battiato, S., Cipolla, R. (eds) Advanced Topics in Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-5520-1_13

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  • DOI: https://doi.org/10.1007/978-1-4471-5520-1_13

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5519-5

  • Online ISBN: 978-1-4471-5520-1

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