Group Association: Assisting Re-identification by Visual Context

Chapter

Abstract

In a crowded public space, people often walk in groups, either with people they know or with strangers. Associating a group of people over space and time can assist understanding an individual’s behaviours as it provides vital visual context for matching individuals within the group. This seems to be an ‘easier’ task compared with person re-identification due to the availability of more and richer visual content in associating a group; however, solving this problem turns out to be rather challenging because a group of people can be highly non-rigid with changing relative position of people within the group and severe self-occlusions. In this work, the problem of matching/associating groups of people over large space and time gaps captured in multiple non-overlapping camera views is addressed. Specifically, a novel people group representation and a group matching algorithm are proposed. The former addresses changes in the relative positions of people in a group and the latter uses the proposed group descriptors for measuring the similarity between two candidate images. Based on group matching, we further formulate a method for matching individual person using the group description as visual context. These methods are validated using the 2008 i-LIDS Multiple-Camera Tracking Scenario (MCTS) dataset on multiple camera views from a busy airport arrival hall.

References

  1. 1.
    Gheissari, N., Sebastian, T.B., Tu, P.H., Rittscher, J., Hartley, R.: Person reidentification using spatiotemporal appearance. In: Procedings of the IEEE Conference on Computer Vision and Pattern Recognition (2006)Google Scholar
  2. 2.
    Hu, W., Hu, M., Zhou, X., Lou, J., Tan, T., Maybank, S.: Principal axis-based correspondence between multiple cameras for people tracking. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 663–671 (2006)CrossRefGoogle Scholar
  3. 3.
    Wang, X., Doretto, G., Sebastian, T., Rittscher, J., Tu, P.: Shape and appearance context modeling. In: Proceedings of the International Conference on Computer Vision (2007)Google Scholar
  4. 4.
    Gray, D., Tao, H.: Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: Proceedings of the European Conference on Computer Vision (2008)Google Scholar
  5. 5.
    Javed, O., Rasheed, Z., Shafique, K., Shah, M.: Tracking across multiple cameras with disjoint views. In: Proceedings of the International Conference on Computer Vision (2003)Google Scholar
  6. 6.
    Madden, C., Cheng, E., Piccardi, M.: Tracking people across disjoint camera views by an illumination-tolerant appearance representation. Mach. Vision Appl. 18(3), 233–247 (2007)CrossRefMATHGoogle Scholar
  7. 7.
    HOSDB: Imagery library for intelligent detection systems (i-lids). In: Proceedings of the IEEE Conference on Crime and Security (2006)Google Scholar
  8. 8.
    Dollar, P., Tu, Z., Tao, H., Belongie, S.: Feature mining for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2007)Google Scholar
  9. 9.
    Gheissari, N., Sebastian, T., Hartley, R.: Person reidentification using spatiotemporal appearance. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2006)Google Scholar
  10. 10.
    Bazzani, L., Cristani, M., Murino, V.: Symmetry-driven accumulation of local features for human characterization and re-identification. Comput. Vis. Image Underst. 117(2), 130–144 (2013)Google Scholar
  11. 11.
    Zheng, W., Gong, S., Xiang, T.: Re-identification by relative distance comparison. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 653–668 (2013)CrossRefGoogle Scholar
  12. 12.
    Prosser, B., Zheng, W., Gong, S., Xiang, T.: Person re-identification by support vector ranking. In: Proceedings of the British Machine Vision Conference (2010)Google Scholar
  13. 13.
    Torralba, A., Murphy, K., Freeman, W., Rubin, M.: Context-based vision system for place and object recognition. In: Proceedings of the International Conference on Computer Vision (2003)Google Scholar
  14. 14.
    Rabinovich, A., Vedaldi, A., Galleguillos, C., Wiewiora, E., Belongie, S.: Objects in context. In: Proceedings of the International Conference on Computer Vision (2007)Google Scholar
  15. 15.
    Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  16. 16.
    Zheng, W., Gong, S., Xiang, T.: Quantifying and transferring contextual information in object detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(4), 762–777 (2012)CrossRefGoogle Scholar
  17. 17.
    Kumar, S., Hebert, M.: A hierarchical field framework for unified context-based classification. In: Proceedings of the International Conference on Computer Vision (2005)Google Scholar
  18. 18.
    Carbonetto, P., de Freitas, N., Barnard, K.: A statistical model for general contextual object recognition. In: Proceedings of the European Conference on Computer Vision (2004)Google Scholar
  19. 19.
    Galleguillos, C., Rabinovich, A., Belongie, S.: Object categorization using co-occurrence, location and appearance. In : Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
  20. 20.
    Gupta, A., Davis, L.S.: Beyond nouns: exploiting prepositions and comparative adjectives for learning visual classifier. In: Proceedings of the European Conference on Computer Vision (2008)Google Scholar
  21. 21.
    Hoiem, D., Efros, A., Hebert, M.: Putting objects in perspective. Int. J. Comput. Vision 80(1), 3–15 (2008)CrossRefGoogle Scholar
  22. 22.
    Bao, S.Y.Z., Sun, M., Savarese, S.: Toward coherent object detection and scene layout understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 65–72 (2010)Google Scholar
  23. 23.
    Galleguillos, C., McFee, B., Belongie, S., Lanckriet, G.: Multi-class object localization by combining local contextual interactions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2010)Google Scholar
  24. 24.
    Vedaldi, A., Gulshan, V., Varma, M., Zisserman, A.: Multiple kernels for object detection. In: Proceedings of the IEEE International Conference on Computer Vision (2009)Google Scholar
  25. 25.
    Brostow, G.J., Cipolla, R.: Unsupervised bayesian detection of independent motion in crowds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2006)Google Scholar
  26. 26.
    Arandjelović, O.: Crowd detection from still images. In: Proceedings of the British Machine Vision Conference (2008)Google Scholar
  27. 27.
    Kong, D., Gray, D., Tao, H.: Counting pedestrians in crowds using viewpoint invariant training. In: Proceedings of the British Machine Vision Conference (2005)Google Scholar
  28. 28.
    Rabaud, V., Belongie, S.: Counting crowded moving objects. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2006)Google Scholar
  29. 29.
    Gong, S., Xiang, T.: Recognition of group activities using dynamic probabilistic networks. In: Proceedings of the International Conference on Computer Vision (2003)Google Scholar
  30. 30.
    Saxena, S., Brémond, F., Thonnat, M., Ma, R.: Crowd behavior recognition for video surveillance. In: Proceedings of the International Conference on Advanced Concepts for Intelligent Vision Systems (2008)Google Scholar
  31. 31.
    Zheng, W., Gong, S., Xiang, T.: Associating groups of people. In: Proceedings of the British Machine Vision Conference (2009)Google Scholar
  32. 32.
    Savarese, S., Winn, J., Criminisi, A.: Discriminative object class models of appearance and shape by correlatons. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2006)Google Scholar
  33. 33.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 2(60), 91–110 (2004)CrossRefGoogle Scholar
  34. 34.
    Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Proceedings of the European Conference on Computer Vision, International Workshop on Statistical Learning in Computer Vision (2004)Google Scholar
  35. 35.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2005)Google Scholar
  36. 36.
    He, R., Zheng, W.S., Hu, B.G.: Maximum correntropy criterion for robust face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1561–1576 (2011)Google Scholar
  37. 37.
    Zheng, W., Gong, S., Xiang, T.: Person re-identification by probabilistic relative distance comparison. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 649–656. Colorado Springs (2011)Google Scholar
  38. 38.
    Chapelle, O., Keerthi, S.S.: Efficient algorithms for ranking with svms. Inf. Retrieval 13(3), 201–215 (2010)CrossRefGoogle Scholar
  39. 39.
    Fowlkes, C., Belongie, S., Chung, F., Malik, J.: Spectral grouping using the nystrom method. IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 214–225 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Sun Yat-sen UniversityGuangzhouChina
  2. 2.Queen Mary University of LondonLondonUK
  3. 3.Queen Mary University of LondonLondonUK

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