Skip to main content
SpringerLink
Log in

Object Tracking Guided by Segmentation Reliability Measures and Local Features

  • Conference paper
  • First Online:
Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 693))

  • 1055 Accesses

Abstract

Real world applications need to cope with unreliable data sources that affect negatively the performance of visual systems, adding error to the whole process. Existing solutions focus their efforts on decreasing the probability of making errors, but if an error occurs, there is no mechanism to deal with it. This work focuses in dealing with this problem by modelling the quality of the segmentation phase in order to apply control mechanisms to mitigate negative effects in later stages. Our control mechanism is based on determining the reliability of local features to discard the less reliables. Local features are characterized using colour, texture, and an illumination reliability model to quantify the quality of illumination. The use of local features enables us to deal with partial occlusion problems by determining the global object position via local features consensus. Experiments were performed, showing promising results in object position estimation under poor illumination conditions.

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

Access this chapter

Log in via an institution
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 EPUB and 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

Notes

  1. 1.

    http://www.alov300.org/.

  2. 2.

    Open dataset extracted from Alfheim Stadium, the home arena for TromsøIL (Norway). Available from: http://home.ifi.uio.no/paalh/dataset/alfheim/.

  3. 3.

    Interdisciplinary Center of Neuroscience of Valparaiso, Chile. http://cinv.uv.cl/en/.

References

  1. Kalal, Z., Matas, J., Mikolajczyk, K.: Tracking learning detection. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1409–1422 (2011)

    Article  Google Scholar 

  2. Yang, F., Lu, H., Yang, M.: Robust superpixel tracking. IEEE Trans. Image Process. 23, 1639–1651 (2014)

    Article  MathSciNet  Google Scholar 

  3. Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: principles and practice of background maintenance. In: Proceedings of the International Conference on Computer Vision (ICCV 1999), pp. 255–261 (1999). doi:10.1109/ICCV.1999.791228

  4. Troya-Galvis, A., Gancarski, P., Passat, N., Berti-Equille, L.: Unsupervised quantification of under- and over-segmentation for object-based remote sensing image analysis. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8, 1936–1945 (2015)

    Article  Google Scholar 

  5. Correia, P.L., Pereira, F.: Objective evaluation of video segmentation quality. IEEE Trans. Image Process. 12, 186–200 (2003)

    Article  Google Scholar 

  6. Erdem, Ç.E., Sankur, B., et al.: Performance measures for video object segmentation and tracking. IEEE Trans. Image Process. 13, 937–951 (2004)

    Article  Google Scholar 

  7. Lee, S., Horio, K.: Human tracking using particle filter with reliable appearance model. In: 2013 Proceedings of SICE Annual Conference (SICE), pp. 1418–1424 (2013)

    Google Scholar 

  8. Wang, L., Yan, H., Wu, H.Y., Pan, C.: Forward-backward mean-shift for visual tracking with local-background-weighted histogram. IEEE Trans. Intell. Transp. Syst. 14, 1480–1489 (2013)

    Article  Google Scholar 

  9. Sun, L., Liu, G.: Visual object tracking based on combination of local description and global representation. IEEE Trans. Circ. Syst. Video Technol. 21, 408–420 (2011)

    Article  Google Scholar 

  10. Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 798–805 (2006)

    Google Scholar 

  11. Breitenstein, M., Reichlin, F., Leibe, B., Koller-Meier, E., Van Gool, L.: Robust tracking-by-detection using a detector confidence particle filter. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1515–1522 (2009)

    Google Scholar 

  12. Zuniga, M.D., Bremond, F., Thonnat, M.: Real-time reliability measure driven multi-hypothesis tracking using 2D and 3D features. EURASIP J. Adv. Signal Process. 2011, 142 (2011). doi:10.1186/1687-6180-2011-142

    Article  Google Scholar 

  13. McIvor, A.: Background subtraction techniques. In: Proceedings of the Conference on Image and Vision Computing (IVCNZ 2000), Hamilton, New Zealand, pp. 147–153 (2000)

    Google Scholar 

  14. Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Proceedings of the IEEE European Conference on Computer Vision (ECCV 2006), vol. 1, pp. 430–443 (2006)

    Google Scholar 

  15. Alahi, A., Ortiz, R., Vandergheynst, P.: Freak: fast retina keypoint. In: Procedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012), pp. 510–517 (2012)

    Google Scholar 

  16. Bhattacharyya, A.: On a measure of divergence between two statistical populations defined by probability distributions. Bull. Calcutta Math. Soc. 35, 99–110 (1943)

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This research has been supported, in part, by Fondecyt Project 11121383, Chile.

Author information

Authors and Affiliations

  1. Department of Computer Science, Universidad Técnica Federico Santa María, Av. España 1680, Valparaíso, Chile

    Cristian M. Orellana

  2. Electronics Department, Universidad Técnica Federico Santa María, Av. España 1680, Valparaíso, Chile

    Marcos D. Zuniga

Authors
  1. Cristian M. Orellana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marcos D. Zuniga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marcos D. Zuniga .

Editor information

Editors and Affiliations

  1. Escola Superior de Tecnologia do IPS, Setúbal, Portugal

    José Braz

  2. MiraLab, University of Geneva, Carouge, Switzerland

    Nadia Magnenat-Thalmann

  3. LISA - ISTIA, University of Angers, Angers, France

    Paul Richard

  4. Department of Computer Science and Electrical Engineering, Jacobs University, Bremen, Germany

    Lars Linsen

  5. University of Groningen, Groningen, The Netherlands

    Alexandru Telea

  6. Università di Catania, Catania, Italy

    Sebastiano Battiato

  7. Research Innovation Center, Canon U.S.A. Inc., San Jose, California, USA

    Francisco Imai

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Orellana, C.M., Zuniga, M.D. (2017). Object Tracking Guided by Segmentation Reliability Measures and Local Features. In: Braz, J., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2016. Communications in Computer and Information Science, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-64870-5_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-64870-5_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64869-9

  • Online ISBN: 978-3-319-64870-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation