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Patch-based topic model for group detection

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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.

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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. 589–597

  2. 2

    Wang Q, Fang J W, Yuan Y. Multi-cue based tracking. Neurocomputing, 2014, 131: 227–236

  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: 562–575

  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. 1–6

  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: 1674–1687

  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. 857–871

  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. 2227–2234

  8. 8

    Zhou B L, Tang X O, Zhang H P, et al. Measuring crowd collectiveness. IEEE Trans Pattern Anal Mach Intell, 2014, 36: 1586–1599

  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. 4147–4153

  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. 4292–4298

  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. 1378–1382

  12. 12

    Blei D, Ng A, Jordan M. Latent dirichlet allocation. J Mach Learn Res, 2003, 3: 993–1022

  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. 1193–1198

  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. 785–798

  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. 3441–3448

  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: 394–406

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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.

Author information

Correspondence to Qi Wang.

Electronic supplementary material

Patch-based Topic Model for Group Detection

Patch-based Topic Model for Group Detection

Patch-based Topic Model for Group Detection

Patch-based Topic Model for Group Detection

Patch-based Topic Model for Group Detection

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Cite this article

Chen, M., Wang, Q. & Li, X. Patch-based topic model for group detection. Sci. China Inf. Sci. 60, 113101 (2017). https://doi.org/10.1007/s11432-017-9237-1

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Keywords

  • group detection
  • collective behavior
  • crowd analysis
  • latent topic