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Patch-based topic model for group detection

  • Mulin Chen
  • Qi WangEmail author
  • Xuelong Li
Moop

Abstract

Pedestrians in crowd scenes tend to connect with each other and form coherent groups. In order to investigate the collective behaviors in crowds, plenty of studies have been conducted on group detection. However, most of the existing methods are limited to discover the underlying semantic priors of individuals. By segmenting the crowd image into patches, this paper proposes the Patch-based Topic Model (PTM) for group detection. The main contributions of this study are threefold: (1) the crowd dynamics are represented by patchlevel descriptor, which provides a macroscopic-level representation; (2) the semantic topic label of each patch are inferred by integrating the Latent Dirichlet Allocation (LDA) model and the Markov Random Fields (MRF); (3) the optimal group number is determined automatically with an intro-class distance evaluation criterion. Experimental results on real-world crowd videos demonstrate the superior performance of the proposed method over the state-of-the-arts.

Keywords

group detection collective behavior crowd analysis latent topic 

Notes

Acknowledgements

This work was supported by National Key Research and Development Program of China (Grant No. 2017YFB1002202), National Natural Science Foundation of China (Grant Nos. 61773316, 61379094), Fundamental Research Funds for the Central Universities (Grant No. 3102017AX010), and Open Research Fund of Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences.

Supplementary material

Patch-based Topic Model for Group Detection

Patch-based Topic Model for Group Detection

11432_2017_9237_MOESM3_ESM.pdf (8.8 mb)
Patch-based Topic Model for Group Detection

References

  1. 1.
    Zhang Y Y, Zhou D, Chen S Q, et al. Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 2016. 589–597Google Scholar
  2. 2.
    Wang Q, Fang J W, Yuan Y. Multi-cue based tracking. Neurocomputing, 2014, 131: 227–236CrossRefGoogle Scholar
  3. 3.
    Yuan Y, Fang J W, Wang Q. Online anomaly detection in crowd scenes via structure analysis. IEEE Trans Syst Man Cybernet, 2015, 45: 562–575Google Scholar
  4. 4.
    Ali S, Shah M. A lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, 2007. 1–6Google Scholar
  5. 5.
    Lin W Y, Mi Y, Wang W Y, et al. A diffusion and clustering-based approach for finding coherent motions and understanding crowd scenes. IEEE Trans Image Process, 2016, 25: 1674–1687MathSciNetCrossRefGoogle Scholar
  6. 6.
    Zhou B L, Tang X O, Wang X G. Coherent filtering: detecting coherent motions from crowd clutters. In: Proceedings of European Conference on Computer Vision, Florence, 2012. 857–871Google Scholar
  7. 7.
    Shao J, Loy C C, Wang X G. Scene-independent group profiling in crowd. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014. 2227–2234Google Scholar
  8. 8.
    Zhou B L, Tang X O, Zhang H P, et al. Measuring crowd collectiveness. IEEE Trans Pattern Anal Mach Intell, 2014, 36: 1586–1599CrossRefGoogle Scholar
  9. 9.
    Li X L, Chen M L, Nie F P, et al. A multiview-based parameter free framework for group detection. In: Proceedings of AAAI Conference on Artificial Intelligence, San Francisco, 2017. 4147–4153Google Scholar
  10. 10.
    Wang Q, Chen M L, Li X L. Quantifying and detecting collective motion by manifold learning. In: Proceedings of AAAI Conference on Artificial Intelligence, San Francisco, 2017. 4292–4298Google Scholar
  11. 11.
    Chen M L, Wang Q, Li X L. Anchor-based group detection in crowd scenes. In: Proceedings of International Conference on Acoustics, Speech and Signal Processing, New Orleans, 2017. 1378–1382Google Scholar
  12. 12.
    Blei D, Ng A, Jordan M. Latent dirichlet allocation. J Mach Learn Res, 2003, 3: 993–1022zbMATHGoogle Scholar
  13. 13.
    Lu H Y, Xie L Y, Kang N, et al. Don’t forget the quantifiable relationship between words: using recurrent neural network for short text topic discovery. In: Proceedings of AAAI Conference on Artificial Intelligence, San Francisco, 2017. 1193–1198Google Scholar
  14. 14.
    Zhao B, Li F-F, Xing E P. Image segmentation with topic random field. In: Proceedings of European Conference on Computer Vision, Heraklion, 2010. 785–798Google Scholar
  15. 15.
    Zhou B L, Wang X G, Tang X O. Random field topic model for semantic region analysis in crowded scenes from tracklets. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, 2011. 3441–3448Google Scholar
  16. 16.
    Lu Z, Yang X K, Lin W Y, et al. Inferring user image-search goals under the implicit guidance of users. IEEE Trans Circ Syst Video Tech, 2014, 24: 394–406CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.School of Computer Science and Center for Optical Imagery Analysis and LearningNorthwestern Polytechnical UniversityXi’anChina
  2. 2.Unmanned System Research InstituteNorthwestern Polytechnical UniversityXi’anChina
  3. 3.Center for Optical Imagery Analysis and Learning, Xi’an Institute of Optics and Precision MechanicsChinese Academy of SciencesXi’anChina

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