Skip to main content

Video Sequence Boundary Labeling with Temporal Coherence

  • Conference paper
  • First Online:
Advances in Computer Graphics (CGI 2019)

Abstract

We propose a method for video sequence boundary labeling which maintains the temporal coherence. The method is based on two ideas. We limit the movement of the label boxes only to the horizontal direction, and reserve free space for the movement of the label boxes in the label layout. The proposed method is able to position label boxes in video sequence on a lower number of rows than existing methods, while at the same time, it minimizes the movement of label boxes. We conducted an extensive user experiment where the proposed method was ranked the best for panorama video sequences labeling compared to three existing methods.

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 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.

    Supplementary material: http://cphoto.fit.vutbr.cz/panorama-labeling/.

References

  1. Agresti, A., Coull, B.A.: Approximate is better than ‘exact’ for interval estimation of binomial proportions. Am. Stat. 52(2), 119–126 (1998)

    MathSciNet  Google Scholar 

  2. Baboud, L., Čadík, M., Eisemann, E., Seidel, H.P.: Automatic photo-to-terrain alignment for the annotation of mountain pictures. In: CVPR 2011, pp. 41–48. IEEE Computer Society, Washington, DC (2011)

    Google Scholar 

  3. Bekos, M.A., Kaufmann, M., Potika, K., Symvonis, A.: Multi-stack boundary labeling problems. WSEAS Trans. Comput. 5(11), 2602–2607 (2006)

    MATH  Google Scholar 

  4. Belotti, P., Kirches, C., Leyffer, S., Linderoth, J., Luedtke, J., Mahajan, A.: Mixed Integer Nonlinear Programming. Cambridge University Press, Cambridge (2012)

    MATH  Google Scholar 

  5. Benkert, M., Haverkort, H., Kroll, M., Nöllenburg, M.: Algorithms for multi-criteria one-sided boundary labeling. In: Hong, S.-H., Nishizeki, T., Quan, W. (eds.) GD 2007. LNCS, vol. 4875, pp. 243–254. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-77537-9_25

    Chapter  Google Scholar 

  6. Chen, D.S., Batson, R.G., Dang, Y.: Applied Integer Programming: Modeling and Solution. Wiley, Hoboken (2011)

    MATH  Google Scholar 

  7. Čmolík, L., Bittner, J.: Layout-aware optimization for interactive labeling of 3D models. Comput. Graph. 34(4), 378–387 (2010)

    Article  Google Scholar 

  8. David, H.: The Method of Paired Comparisons. Griffin’s Statistical Monographs & Courses, C. Griffin (1988)

    Google Scholar 

  9. Garey, M.R., Johnson, D.S.: Computers and Intractability; A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., New York (1990)

    MATH  Google Scholar 

  10. Gemsa, A., Haunertand, J.H., Nöllenburg, M.: Multi-row boundary-labeling algorithms for panorama images. ACM TSAS 1(1), 289–298 (2014)

    Google Scholar 

  11. Götzelmann, T., Hartmann, K., Strothotte, T.: Annotation of animated 3D objects. In: SimVis. SCS, pp. 209–222. Publishing House (2007)

    Google Scholar 

  12. Gurobi Optimization, LLC: Advanced Gurobi Algorithms (2016). http://www.gurobi.com/pdfs/user-events/2016-frankfurt/Die-Algorithmen.pdf

  13. Kouřil, D., et al.: Labels on levels: labeling of multi-scale multi-instance and crowded 3D biological environments. IEEE TVCG 25(1), 977–986 (2019)

    Google Scholar 

  14. Lewis, J.R., Sauro, J.: When 100% really isn’t 100%: improving the accuracy of small-sample estimates of completion rates. J. Usability Stud. 1(3), 136–150 (2006)

    Google Scholar 

  15. Maass, S., Döllner, J.: Efficient view management for dynamic annotation placement in virtual landscapes. In: Butz, A., Fisher, B., Krüger, A., Olivier, P. (eds.) SG 2006. LNCS, vol. 4073, pp. 1–12. Springer, Heidelberg (2006). https://doi.org/10.1007/11795018_1

    Chapter  Google Scholar 

  16. MacKenzie, I.S.: Human-Computer Interaction: An Empirical Research Perspective. Newnes, Oxford (2012)

    Google Scholar 

  17. Perez-Ortiz, M., Mantiuk, R.K.: A practical guide and software for analysing pairwise comparison experiments (2017). http://arxiv.org/abs/1712.03686

  18. Sauro, J., Lewis, J.R.: Estimating completion rates from small samples using binomial confidence intervals: comparisons and recommendations. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 49, no. 24, pp. 2100–2103. SAGE Publications, Thousand Oaks (2005)

    Article  Google Scholar 

  19. Sauro, J., Lewis, J.R.: Quantifying the User Experience: Practical Statistics for User Research. Elsevier, Amsterdam (2012)

    Google Scholar 

  20. Tatzgern, M., Kalkofen, D., Grasset, R., Schmalstieg, D.: Hedgehog labeling: view management techniques for external labels in 3D space. In: 2014 IEEE Virtual Reality, pp. 27–32 (2014)

    Google Scholar 

  21. Tsukida, K., Gupta, M.R.: How to analyze paired comparison data. UWEE Technical report 206 (2011)

    Google Scholar 

  22. Vaaraniemi, M., Treib, M., Westermann, R.: Temporally coherent real-time labeling of dynamic scenes. In: Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications, COM.Geo 2012, pp. 17:1–17:10. ACM, New York (2012)

    Google Scholar 

  23. Ye, Y., Tse, E.: An extension of Karmarkar’s projective algorithm for convex quadratic programming. Math. Program. 44(1), 157–179 (1989)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by Research Center for Informatics No. CZ.02.1.01/0.0/0.0/16_019/0000765; by V3C – “Visual Computing Competence Center” by Technology Agency of the Czech Republic, project no. TE01020415; by the Ministry of Education, Youth and Sports of the Czech Republic within the activity MOBILITY (MSMT-539/2017-1) ID: 7AMB17AT021, and from the “National Programme of Sustainability (NPU II) project IT4Innovations excellence in science - LQ1602”; and by the IT4Innovations infrastructure which is supported from the Large Infrastructures for Research, Experimental Development and Innovations project “IT4Innovations National Supercomputing Center - LM2015070”. Access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Petr Bobák .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bobák, P., Čmolík, L., Čadík, M. (2019). Video Sequence Boundary Labeling with Temporal Coherence. In: Gavrilova, M., Chang, J., Thalmann, N., Hitzer, E., Ishikawa, H. (eds) Advances in Computer Graphics. CGI 2019. Lecture Notes in Computer Science(), vol 11542. Springer, Cham. https://doi.org/10.1007/978-3-030-22514-8_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-22514-8_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22513-1

  • Online ISBN: 978-3-030-22514-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics