Skip to main content

A Web-Service for Object Detection Using Hierarchical Models

  • Conference paper
Computer Vision Systems (ICVS 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7963))

Included in the following conference series:

Abstract

This paper proposes an architecture for an object detection system suitable for a web-service running distributed on a cluster of machines. We build on top of a recently proposed architecture for distributed visual recognition system and extend it with the object detection algorithm. As sliding-window techniques are computationally unsuitable for web-services we rely on models based on state-of-the-art hierarchical compositions for the object detection algorithm. We provide implementation details for running hierarchical models on top of a distributed platform and propose an additional hypothesis verification step to reduce many false-positives that are common in hierarchical models. For a verification we rely on a state-of-the-art descriptor extracted from the hierarchical structure and use a support vector machine for object classification. We evaluate the system on a cluster of 80 workers and show a response time of around 10 seconds at throughput of around 60 requests per minute.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Chang, C.C., Lin, C.J.: Libsvm: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

  2. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Transactions on PAMI 32, 1627–1645 (2010)

    Article  Google Scholar 

  3. Ferrari, V., Tuytelaars, T., Van Gool, L.: Object detection by contour segment networks. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 14–28. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  4. Fidler, S., Boben, M., Leonardis, A.: Evaluating multi-class learning strategies in a generative hierarchical framework for object detection. In: NIPS (2009)

    Google Scholar 

  5. Fidler, S., Leonardis, A.: Towards scalable representations of object categories: Learning a hierarchy of parts. In: CVPR. IEEE Computer Society (2007)

    Google Scholar 

  6. Kokkinos, I., Yuille, A.: Inference and learning with hierarchical shape models. Int. J. Comput. Vision 93(2), 201–225 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  7. Lampert, C.H., Blaschko, M.B., Hofmann, T.: Beyond sliding windows: Object localization by efficient subwindow search. In: CVPR, pp. 1–8 (2008)

    Google Scholar 

  8. Tabernik, D., Kristan, M., Boben, M., Leonardis, A.: Learning statistically relevant edge structure improves low-level visual descriptors. In: ICPR (2012)

    Google Scholar 

  9. Tabernik, D., Čehovin, L., Kristan, M., Boben, M., Leonardis, A.: Vicos eye - a webservice for visual object categorization. In: Proc. of the 18th Computer Vision Winter Workshop (2013)

    Google Scholar 

  10. Wojek, C., Dorkó, G., Schulz, A., Schiele, B.: Sliding-windows for rapid object class localization: A parallel technique. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 71–81. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  11. Zhu, L., Chen, Y., Torralba, A., Freeman, W., Yuille, A.: Part and appearance sharing: Recursive compositional models for multi-view. In: CVPR, pp. 1919–1926 (June 2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Tabernik, D., Čehovin, L., Kristan, M., Boben, M., Leonardis, A. (2013). A Web-Service for Object Detection Using Hierarchical Models. In: Chen, M., Leibe, B., Neumann, B. (eds) Computer Vision Systems. ICVS 2013. Lecture Notes in Computer Science, vol 7963. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39402-7_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-39402-7_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39401-0

  • Online ISBN: 978-3-642-39402-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics