Skip to main content
SpringerLink
Log in

Analysis of object description methods in a video object tracking environment

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

A key issue in video object tracking is the representation of the objects and how effectively it discriminates between different objects. Several techniques have been proposed, but without a generally accepted method. While analysis and comparisons of these individual methods have been presented in the literature, their evaluation as part of a global solution has been overlooked. The appearance model for the objects is a component of a video object tracking framework, depending on previous processing stages and affecting those that succeed it. As a result, these interdependencies should be taken into account when analysing the performance of the object description techniques. We propose an integrated analysis of object descriptors and appearance models through their comparison in a common object tracking solution. The goal is to contribute to a better understanding of object description methods and their impact on the tracking process. Our contributions are threefold: propose a novel descriptor evaluation and characterisation paradigm; perform the first integrated analysis of state-of-the-art description methods in a scenario of people tracking; put forward some ideas for appearance models to use in this context. This work provides foundations for future tests and the proposed assessment approach contributes to the informed selection of techniques more adequately for a given tracking application context.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. The reader may note some similarities with the architecture proposed by Moeslund [26].

    Fig. 1
    figure 1

    Conceptual generic architecture of a video object tracking solution

    Full size image

References

  1. Alahi, A., Vandergheynst, P., Bierlaire, M., Kunt, M.: Cascade of descriptors to detect and track objects across any network of cameras. Comput. Vis. Image Underst. 114, 624–640 (2010)

    Article  Google Scholar 

  2. Bashir, F., Porikli, F.: Performance evaluation of object detection and tracking systems. In: PETS, Proceedings of IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (2006)

  3. Bastos, R., Dias, M.S.: FIRST–Fast Invariant to Rotation and Scale Transform: Invariant Image Features for Augmented Reality and Computer Vision. VDM Verlag, Saarbrucken (2009)

    Google Scholar 

  4. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110, 346–359 (2008)

    Article  Google Scholar 

  5. Black, J., Ellis, T., Rosin, P.: A novel method for video tracking performance evaluation. In: Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS), pp. 125–132 (2003)

  6. Bradski, G.R.: Computer vision face tracking for use in a perceptual user interface. Intel Technol. J. (Q2) (1998)

  7. Brown, M., Lowe, D.: Invariant features from interest point groups. In: British Machine Vision Conference, pp. 656–665 (2002)

  8. Cardoso, J.S., Carvalho, P., Teixeira, L.F., Corte-Real, L.: Partition-distance methods for assessing spatial segmentations of images and videos. Comput. Vis. Image Underst. 113(7), 811–823 (2009)

    Article  Google Scholar 

  9. Carvalho, P., Cardoso, J.S., Corte-Real, L.: Hybrid framework for evaluating video object tracking algorithms. Electron. Lett. 46(6), 411–412 (2010). http://www.inescporto.pt/jsc/publications/journals/2010PCarvalhoIET.pdf

    Google Scholar 

  10. Carvalho, P., Cardoso, J.S., Corte-Real, L.: Filling the gap in quality assessment of video object tracking. Image Vis. Comput. 30(9), 630–640 (2012). doi:10.1016/j.imavis.2012.06.002

    Article  Google Scholar 

  11. Caviar: Ec-funded-caviar-project, i. 2001–37540 (2004). http://homepages.inf.ed.ac.uk/rbf/CAVIAR/

  12. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, CVPR ’05, pp. 886–893. IEEE Computer Society, Washington (2005)

  13. Denman, S., Fookes, C., Sridharan, S., Lakemond, R.: Dynamic performance measures for object tracking systems. In: Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS ’09, pp. 541–546. IEEE Computer Society, Washington (2009)

  14. Ellis, T.: Performance metrics and methods for tracking in surveillance. In: 3rd IEEE International Workshop on Performance Evaluation of Tracking and Surveillance PETS’2002. Copenhagen, Denmark (2002)

  15. Erdem, Ç.E., Sankur, B., Tekalp, A.M.: Performance measures for video object segmentation and tracking. IEEE Trans. Image Process. 13(7), 937–951 (2004)

    Article  Google Scholar 

  16. Han, Z., Ye, Q., Jiao, J.: Combined feature evaluation for adaptive visual object tracking. Comput. Vis. Image Underst. 115, 69–80 (2011)

    Article  Google Scholar 

  17. Jiang, Y.G., Yang, J., Ngo, C.W., Hauptmann, A.G.: Representations of Keypoint-Based semantic concept detection: a comprehensive study. IEEE Trans. Multimed. 12(1), 42–53 (2009)

    Article  Google Scholar 

  18. Jiang, Z., Huynh, D.Q., Moran, W., Challa, S., Spadaccini, N.: Multiple pedestrian tracking using colour and motion models. Digit. Image Comput. Tech. Appl. 328–334 (2010)

  19. Ke, Y., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR’04, pp. 506–513. IEEE Computer Society, Washington (2004)

  20. Lazarevic-McManus, N., Renno, J.R., Makris, D., Jones, G.A.: An object-based comparative methodology for motion detection based on the F-Measure. Comput. Vis. Image Underst. 111(1), 74–85 (2008)

    Article  Google Scholar 

  21. List, T., Fisher, R.B.: CVML–an XML-based computer vision markup language. In: Proceedings of the Pattern Recognition, 17th International Conference on (ICPR’04) vol. 1, ICPR ’04, pp. 789–792. IEEE Computer Society, Washington (2004)

  22. Liu, H., Yu, Z., Zha, H., Zou, Y., Zhang, L.: Robust human tracking based on multi-cue integration and mean-shift. Pattern Recognit. Lett. 30 (2009)

  23. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  24. Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int. J. Comput. Vis. 60, 63–86 (2004)

    Article  Google Scholar 

  25. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 2(10), 1615–1630 (2005)

    Article  Google Scholar 

  26. Moeslund, T.B., Granum, E.: A survey of computer vision-based human motion capture. Comput. Vis. Image Underst. 81, 231–268 (2001)

    Article  MATH  Google Scholar 

  27. Nghiem, A.T., Bremond, F., Thonnat, M., Valentin, V.: Etiseo, performance evaluation for video surveillance systems. In: Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 476–481. IEEE Computer Society, Washington (2007)

  28. Opelt, A., Pinz, A., Zisserman, A.: Incremental learning of object detectors using a visual shape alphabet. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 1, 3–10 (2006)

    Google Scholar 

  29. PETS: IEEE international workshop on performance evaluation of tracking and surveillance (2006). http://www.cvg.rdg.ac.uk/PETS2006/index.html

  30. Schlogl, T., Beleznai, C., Winter, M., Bischof, H.: Performance evaluation metrics for motion detection and tracking. In: ICPR ’04: Proceedings of the Pattern Recognition, 17th International Conference on (ICPR’04), vol. 4, pp. 519–522. IEEE Computer Society, Washington (2004)

  31. Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. Int. J. Comput. Vis. 37, 151–172 (2000)

    Article  MATH  Google Scholar 

  32. Shahed, S.M.N., Ho, J., Yang, M.H.: Online visual tracking with histograms and articulating blocks. Comput. Vis. Image Underst. 114(8), 901–914 (2010)

    Article  Google Scholar 

  33. Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W.T.: Discovering objects and their localization in images. In: Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV’05), vol. 1, pp. 370–377. IEEE Computer Society, Washington (2005)

  34. Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, New York (2010)

    Google Scholar 

  35. Tang, F., Tao, H.: Object tracking with dynamic feature graph. In: ICCCN ’05: Proceedings of the 14th International Conference on Computer Communications and, Networks, pp. 25–32 (2005)

  36. Teixeira, L., Carvalho, P., Cardoso, J., Corte-Real, L.: Automatic description of object appearances in a wide-area surveillance scenario. In: 19th IEEE International Conference on Image Processing (ICIP), pp. 1609–1612 (2012)

  37. Teixeira, L.F., Cardoso, J.S., Corte-Real, L.: Object segmentation using background modelling and cascaded change detection. J. Multimed. (JMM) 2, 55–65 (2007)

    Google Scholar 

  38. Tell, D., Carlsson, S.: Combining appearance and topology for wide baseline matching. In: Proceedings of the 7th European Conference on Computer Vision-Part I, ECCV ’02, pp. 68–81. Springer, London (2002)

  39. Tissainayagam, P., Suter, D.: Assessing the performance of corner detectors for point feature tracking applications. Image Vis. Comput. 22, 663–679 (2004)

    Article  Google Scholar 

  40. Venetianer, P.L., Deng, H.: Performance evaluation of an intelligent video surveillance system—a case study. Comput. Vis. Image Underst. 114, 1292–1302 (2010)

    Article  Google Scholar 

  41. Vizireanu, D.N.: Generalizations of binary morphological shape decomposition. J. Electron. Imaging 16, 013,002 (2007)

  42. Vizireanu, N., Halunga, S., Marghescu, G.: Morphological skeleton decomposition interframe interpolation method. J. Electron. Imaging 19, 023,018 (2010)

    Google Scholar 

  43. Wu, L., Hu, Y., Li, M., Yu, N., Hua, X.S.: Scale-invariant visual language modeling for object categorization. IEEE Trans. Multimed. 11, 286–294 (2009)

    Article  Google Scholar 

  44. Zhao, T.: Model-based segmentation and tracking of multiple humans in complex situations. Ph.D. thesis, Faculty of the Graduate School of the University of Southern California (2004)

  45. Zhao, T., Nevatia, R.: Tracking multiple humans in complex situations. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1208–1211 (2004)

    Google Scholar 

  46. Zhou, H., Yuan, Y., Shi, C.: Object tracking using SIFT features and mean shift. Comput. Vis. Image Underst. 113, 345–352 (2009)

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Fundação para a Ciência e a Tecnologia (FCT) - Portugal - and the European Commission, for financing part of this work through the grants SFRH/BD/31259/2006, SFRH/BD/73667/2010 and Fundo Social Europeu (FSE). The work was partially supported by Project: QREN 7900 LUL (Living Usability Lab), a co-promotion R&D projects funded by European Structural Funds for Portugal (FEDER) through COMPETE as part of the National Strategic Reference Framework (QREN), and managed by Agência de Inovação (ADI); QREN 13852 AAL4ALL (Ambient Assisted Living for All), co-financed by the European Community Fund FEDER through COMPETE – Programa Operacional Factores de Competitividade (POFC).

Author information

Authors and Affiliations

  1. INESC TEC (formerly INESC Porto) and Faculdade de Engenharia, Universidade do Porto, Campus da FEUP, Rua Dr. Roberto Frias, no. 378, 4200-465 , Porto, Portugal

    Pedro Carvalho, Telmo Oliveira, Lucian Ciobanu, Jaime S. Cardoso & Luís Côrte-Real

  2. ADETTI-IUL/ISCTE-Lisbon University Institute, Av. das Forças Armadas, Edifício ISCTE, 1600-082 , Lisbon, Portugal

    Filipe Gaspar

  3. Departamento de Engenharia Informática, Faculdade de Engenharia, Universidade do Porto, Campus da FEUP, Rua Dr. Roberto Frias, no. 378, 4200-465 , Porto, Portugal

    Luís F. Teixeira

  4. eyenov, ADETTI-IUL/ISCTE-Lisbon University Institute, Rua Helena Vaz da Silva, no. 32 2C, 1750 , Lisboa, Portugal

    Rafael Bastos

  5. Microsoft Language Development Center and ISCTE-Lisbon University Institute, Av. das Forças Armadas, Edifício ISCTE, 1600-082 , Lisbon, Portugal

    Miguel S. Dias

Authors
  1. Pedro Carvalho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Telmo Oliveira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lucian Ciobanu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Filipe Gaspar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Luís F. Teixeira
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rafael Bastos
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jaime S. Cardoso
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Miguel S. Dias
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Luís Côrte-Real
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pedro Carvalho.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Carvalho, P., Oliveira, T., Ciobanu, L. et al. Analysis of object description methods in a video object tracking environment. Machine Vision and Applications 24, 1149–1165 (2013). https://doi.org/10.1007/s00138-013-0523-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-013-0523-z

Keywords

Search

Navigation