Event Detection Using Quantized Binary Code and Spatial-Temporal Locality Preserving Projections

  • Hanhe Lin
  • Jeremiah D. Deng
  • Brendon J. Woodford
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8272)


We propose a new video manifold learning method for event recognition and anomaly detection in crowd scenes. A novel feature descriptor is proposed to encode regional optical flow features of video frames, where quantization and binarization of the feature code are employed to improve the differentiation of crowd motion patterns. Based on the new feature code, we introduce a new linear dimensionality reduction algorithm called “Spatial-Temporal Locality Preserving Projections” (STLPP). The generated low-dimensional video manifolds preserve both intrinsic spatial and temporal properties. Extensive experiments have been carried out on two benchmark datasets and our results compare favourably with the state of the art.


manifold learning event recognition anomaly detection 


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  1. 1.
    Nguyen, H.T., Ji, Q., Smeulders, A.W.: Spatio-temporal context for robust multitarget tracking. IEEE TPAMI 29(1), 52–64 (2007)CrossRefGoogle Scholar
  2. 2.
    Andrade, E.L., Blunsden, S., Fisher, R.B.: Modelling crowd scenes for event detection. In: Proc. ICPR 2006, vol. 1, pp. 175–178 (2006)Google Scholar
  3. 3.
    Adam, A., Rivlin, E., Shimshoni, I., Reinitz, D.: Robust real-time unusual event detection using multiple fixed-location monitors. IEEE TPAMI 30(3), 555–560 (2008)CrossRefGoogle Scholar
  4. 4.
    Kratz, L., Nishino, K.: Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In: Proc. CVPR 2009, pp. 1446–1453 (2009)Google Scholar
  5. 5.
    Ke, Y., Sukthankar, R., Hebert, M.: Event detection in crowded videos. In: Proc. ICCV 2007, pp. 1–8 (2007)Google Scholar
  6. 6.
    Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: Proc. CVPR 2009, pp. 935–942 (2009)Google Scholar
  7. 7.
    Wu, S., Moore, B.E., Shah, M.: Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes. In: Proc. CVPR 2010, pp. 2054–2060 (2010)Google Scholar
  8. 8.
    Cong, Y., Yuan, J., Liu, J.: Sparse reconstruction cost for abnormal event detection. In: Proc. CVPR 2011, pp. 3449–3456 (2011)Google Scholar
  9. 9.
    Tziakos, I., Cavallaro, A., Xu, L.Q.: Event monitoring via local motion abnormality detection in non-linear subspace. Neurocomputing 73(10), 1881–1891 (2010)CrossRefGoogle Scholar
  10. 10.
    Thida, M., Eng, H.-L., Dorothy, M., Remagnino, P.: Learning video manifold for segmenting crowd events and abnormality detection. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part I. LNCS, vol. 6492, pp. 439–449. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)CrossRefzbMATHGoogle Scholar
  12. 12.
    Thida, M., Eng, H.L., Monekosso, D.N., Remagnino, P.: Learning video manifolds for content analysis of crowded scenes. IPSJ Transactions on Computer Vision and Applications 4, 71–77 (2012)CrossRefGoogle Scholar
  13. 13.
    Liu, C., Freeman, W.T., Adelson, E.H., Weiss, Y.: Human-assisted motion annotation. In: Proc. CVPR 2008, pp. 1–8 (2008)Google Scholar
  14. 14.
    Niyogi, X.: Locality preserving projections. Neural Information Processing Systems 16, 153 (2004)Google Scholar
  15. 15.
    Golub, G.H., van Loan, C.F.: Matrix computations (1996)Google Scholar
  16. 16.
    Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Computation 13(7), 1443–1471 (2001)CrossRefzbMATHGoogle Scholar
  17. 17.
    Garate, C., Bilinsky, P., Bremond, F.: Crowd event recognition using hog tracker. In: Proc. PETS-Winter 2009, pp. 1–6 (2009)Google Scholar
  18. 18.
    Chan, A.B., Morrow, M., Vasconcelos, N.: Analysis of crowded scenes using holistic properties. In: Proc. PETS-Winter 2009, pp. 101–108 (2009)Google Scholar
  19. 19.
    Shi, Y., Gao, Y., Wang, R.: Real-time abnormal event detection in complicated scenes. In: Proc. ICPR 2010, pp. 3653–3656 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Hanhe Lin
    • 1
  • Jeremiah D. Deng
    • 1
  • Brendon J. Woodford
    • 1
  1. 1.Department of Information ScienceUniversity of OtagoDunedinNew Zealand

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