Coloring Channel Representations for Visual Tracking

  • Martin Danelljan
  • Gustav Häger
  • Fahad Shahbaz Khan
  • Michael Felsberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9127)

Abstract

Visual object tracking is a classical, but still open research problem in computer vision, with many real world applications. The problem is challenging due to several factors, such as illumination variation, occlusions, camera motion and appearance changes. Such problems can be alleviated by constructing robust, discriminative and computationally efficient visual features. Recently, biologically-inspired channel representations [9] have shown to provide promising results in many applications ranging from autonomous driving to visual tracking.

This paper investigates the problem of coloring channel representations for visual tracking. We evaluate two strategies, channel concatenation and channel product, to construct channel coded color representations. The proposed channel coded color representations are generic and can be used beyond tracking.

Experiments are performed on 41 challenging benchmark videos. Our experiments clearly suggest that a careful selection of color feature together with an optimal fusion strategy, significantly outperforms the standard luminance based channel representation. Finally, we show promising results compared to state-of-the-art tracking methods in the literature.

Keywords

Visual tracking Channel coding Color names 

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References

  1. 1.
    Adam, A., Rivlin, E., Shimshoni: Robust fragments-based tracking using the integral histogram. In: CVPR (2006)Google Scholar
  2. 2.
    Bao, C., Wu, Y., Ling, H., Ji, H.: Real time robust l1 tracker using accelerated proximal gradient approach. In: CVPR (2012)Google Scholar
  3. 3.
    Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: CVPR (2010)Google Scholar
  4. 4.
    Danelljan, M., Häger, G., Khan, F.S., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: BMVC (2014)Google Scholar
  5. 5.
    Danelljan, M., et al.: A low-level active vision framework for collaborative unmanned aircraft systems. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014 Workshops. LNCS, vol. 8925, pp. 223–237. Springer, Heidelberg (2015) CrossRefGoogle Scholar
  6. 6.
    Danelljan, M., Khan, F.S., Felsberg, M., van de Weijer, J.: Adaptive color attributes for real-time visual tracking. In: CVPR (2014)Google Scholar
  7. 7.
    Dinh, T.B., Vo, N., Medioni, G.: Context tracker: exploring supporters and distracters in unconstrained environments. In: CVPR (2011)Google Scholar
  8. 8.
    Felsberg, M.: Enhanced distribution field tracking using channel representations. In: ICCV Workshop (2013)Google Scholar
  9. 9.
    Felsberg, M., Forssen, P.E., Scharr, H.: Channel smoothing: efficient robust smoothing of low-level signal features. PAMI 28(2), 209–222 (2006)CrossRefGoogle Scholar
  10. 10.
    Felsberg, M., Hedborg, J.: Real-time visual recognition of objects and scenes using P-channel matching. In: Ersbøll, B.K., Pedersen, K.S. (eds.) SCIA 2007. LNCS, vol. 4522, pp. 908–917. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  11. 11.
    Öfjäll, K., Felsberg, M.: Biologically inspired online learning of visual autonomous driving. In: BMVC (2014)Google Scholar
  12. 12.
    Forssen, P.E., Granlund, G., Wiklund, J.: Channel representation of colour images. Tech. rep., Linköping University (2002)Google Scholar
  13. 13.
    Granlund, G.H.: An associative perception-action structure using a localized space variant information representation. In: Sommer, G., Zeevi, Y.Y. (eds.) AFPAC 2000. LNCS, vol. 1888, pp. 48–68. Springer, Heidelberg (2000) CrossRefGoogle Scholar
  14. 14.
    Hare, S., Saffari, A., Torr, P.: Struck: Structured output tracking with kernels. In: ICCV (2011)Google Scholar
  15. 15.
    He, S., Yang, Q., Lau, R., Wang, J., Yang, M.H.: Visual tracking via locality sensitive histograms. In: CVPR (2013)Google Scholar
  16. 16.
    Heinemann, C., Åström, F., Baravdish, G., Krajsek, K., Felsberg, M., Scharr, H.: Using channel representations in regularization terms: a case study on image diffusion. In: VISAPP (2014)Google Scholar
  17. 17.
    Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 702–715. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  18. 18.
    Jia, X., Lu, H., Yang, M.H.: Visual tracking via adaptive structural local sparse appearance model. In: CVPR (2012)Google Scholar
  19. 19.
    Jonsson, E.: Channel-Coded Feature Maps for Computer Vision and Machine Learning. Linköping Studies in Science and Technology. Dissertations No. 1160, Linköping University, Sweden (2008)Google Scholar
  20. 20.
    Kalal, Z., Matas, J., Mikolajczyk, K.: P-n learning: bootstrapping binary classifiers by structural constraints. In: CVPR (2010)Google Scholar
  21. 21.
    Khan, F.S., Anwer, R.M., van de Weijer, J., Bagdanov, A., Lopez, A., Felsberg, M.: Coloring action recognition in still images. IJCV 105(3), 205–221 (2013)CrossRefGoogle Scholar
  22. 22.
    Khan, F.S., van de Weijer, J., Vanrell, M.: Modulating shape features by color attention for object recognition. IJCV 98(1), 49–64 (2012)CrossRefGoogle Scholar
  23. 23.
    Kristan, M., et al.: The visual object tracking VOT2014 challenge results. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014 Workshops. LNCS, vol. 8926, pp. 191–217. Springer, Heidelberg (2015) CrossRefGoogle Scholar
  24. 24.
    Kwon, J., Lee, K.M.: Tracking by sampling trackers. In: ICCV (2011)Google Scholar
  25. 25.
    Liu, B., Huang, J., Yang, L., Kulikowski, C.: Robust tracking using local sparse appearance model and k-selection. In: CVPR (2011)Google Scholar
  26. 26.
    Meneghetti, G., Danelljan, M., Felsberg, M., Nordberg, K.: Image alignment for panorama stitching in sparsely structured environments. In: SCIA (2015)Google Scholar
  27. 27.
    Nummiaro, K., Koller-Meier, E., Gool, L.J.V.: An adaptive color-based particle filter. IVC 21(1), 99–110 (2003)CrossRefGoogle Scholar
  28. 28.
    Oron, S., Hillel, A., Levi, Avidan, S.: Locally orderless tracking. In: CVPR (2012)Google Scholar
  29. 29.
    Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002) CrossRefGoogle Scholar
  30. 30.
    Prokaj, J., Medioni, G.: Persistent tracking for wide area aerial surveillance. In: CVPR (2014)Google Scholar
  31. 31.
    Sevilla-Lara, L., Miller, E.: Distribution fields for tracking. In: CVPR (2012)Google Scholar
  32. 32.
    Wang, D., Lu, H., Yang, M.H.: Least soft-threshold squares tracking. In: CVPR (2013)Google Scholar
  33. 33.
    van de Weijer, J., Schmid, C., Verbeek, J.J., Larlus, D.: Learning color names for real-world applications. TIP 18(7), 1512–1524 (2009)Google Scholar
  34. 34.
    Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: CVPR (2013)Google Scholar
  35. 35.
    Zhang, K., Zhang, L., Yang, M.-H.: Real-time compressive tracking. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 864–877. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  36. 36.
    Zhong, W., Lu, H., Yang, M.H.: Robust object tracking via sparsity-based collaborative model. In: CVPR (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Martin Danelljan
    • 1
  • Gustav Häger
    • 1
  • Fahad Shahbaz Khan
    • 1
  • Michael Felsberg
    • 1
  1. 1.Computer Vision LaboratoryLinköping UniversityLinköpingSweden

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