Advertisement

Online Learned Discriminative Part-Based Appearance Models for Multi-human Tracking

  • Bo Yang
  • Ram Nevatia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7572)

Abstract

We introduce an online learning approach to produce discriminative part-based appearance models (DPAMs) for tracking multiple humans in real scenes by incorporating association based and category free tracking methods. Detection responses are gradually associated into tracklets in multiple levels to produce final tracks. Unlike most previous multi-target tracking approaches which do not explicitly consider occlusions in appearance modeling, we introduce a part based model that explicitly finds unoccluded parts by occlusion reasoning in each frame, so that occluded parts are removed in appearance modeling. Then DPAMs for each tracklet is online learned to distinguish a tracklet with others as well as the background, and is further used in a conservative category free tracking approach to partially overcome the missed detection problem as well as to reduce difficulties in tracklet associations under long gaps. We evaluate our approach on three public data sets, and show significant improvements compared with state-of-art methods.

Keywords

multi-human tracking online learned discriminative models 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Shitrit, H.B., Berclaz, J., Fleuret, F., Fua, P.: Tracking multiple people under global appearance constraints. In: ICCV (2011)Google Scholar
  2. 2.
    Yang, B., Huang, C., Nevatia, R.: Learning affinities and dependencies for multi-target tracking using a crf model. In: CVPR (2011)Google Scholar
  3. 3.
    Benfold, B., Reid, I.: Stable multi-target tracking in real-time surveillance video. In: CVPR (2011)Google Scholar
  4. 4.
    Pirsiavash, H., Ramanan, D., Fowlkes, C.C.: Globally-optimal greedy algorithms for tracking a variable number of objects. In: CVPR (2011)Google Scholar
  5. 5.
    Song, B., Jeng, T.-Y., Staudt, E., Roy-Chowdhury, A.K.: A Stochastic Graph Evolution Framework for Robust Multi-target Tracking. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 605–619. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Wang, S., Lu, H., Yang, F., Yang, M.H.: Superpixel tracking. In: ICCV (2011)Google Scholar
  7. 7.
    Liu, B., Huang, J., Yang, L., Kulikowsk, C.: Robust tracking using local sparse appearance model and k-selection. In: CVPR (2011)Google Scholar
  8. 8.
    Grabner, H., Matas, J., Gool, L.V., Cattin, P.: Tracking the invisible: Learning where the object might be. In: CVPR (2010)Google Scholar
  9. 9.
    Xing, J., Ai, H., Lao, S.: Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses. In: CVPR (2009)Google Scholar
  10. 10.
    Kuo, C.H., Nevatia, R.: How does person identity recognition help multi-person tracking? In: CVPR (2011)Google Scholar
  11. 11.
    Pellegrini, S., Ess, A., Schindler, K., Gool, L.V.: You’ll neverwalk alone: Modeling social behavior for multi-target tracking. In: ICCV (2009)Google Scholar
  12. 12.
    Stalder, S., Grabner, H., Van Gool, L.: Cascaded Confidence Filtering for Improved Tracking-by-Detection. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 369–382. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Kuo, C.H., Huang, C., Nevatia, R.: Multi-target tracking by on-line learned discriminative appearance models. In: CVPR (2010)Google Scholar
  14. 14.
    Song, X., Shao, X., Zhao, H., Cui, J., Shibasaki, R., Zha, H.: An online approach: Learning-semantic-scene-by-tracking and tracking-by-learning-semantic-scene. In: CVPR (2010)Google Scholar
  15. 15.
    Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Gool, L.V.: Online multi-person tracking-by-detection from a single, uncalibrated camera. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 1820–1833 (2011)CrossRefGoogle Scholar
  16. 16.
    Ramanan, D., Forsyth, D.A., Zisserman, A.: Tracking people by learning their appearance. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 65–81 (2007)CrossRefGoogle Scholar
  17. 17.
    Wu, B., Nevatia, R.: Detection and segmentation of multiple, partially occluded objects by grouping, merging, assigning part detection responses. International Journal of Computer Vision 82, 185–204 (2009)CrossRefGoogle Scholar
  18. 18.
    Kalal, Z., Matas, J., Mikolajczyk, K.: P-n learning: Bootstrapping binary classifiers by structural constraints. In: CVPR (2010)Google Scholar
  19. 19.
    Pets 2009 dataset (2009), http://www.cvg.rdg.ac.uk/PETS2009
  20. 20.
    Ess, A., Bastian Leibe, K.S., van Gool, L.: Robust multiperson tracking from a mobile platform. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 1831–1846 (2009)CrossRefGoogle Scholar
  21. 21.
    Andriyenko, A., Schindler, K.: Multi-target tracking by continuous energy minimization. In: CVPR (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bo Yang
    • 1
  • Ram Nevatia
    • 1
  1. 1.Institute for Robotics and Intelligent SystemsUniversity of Southern CaliforniaLos AngelesUSA

Personalised recommendations