Bayesian Multimodal Fusion in Forensic Applications

  • Virginia Fernandez Arguedas
  • Qianni Zhang
  • Ebroul Izquierdo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7585)

Abstract

The public location of CCTV cameras and their connexion with public safety demand high robustness and reliability from surveillance systems. This paper focuses on the development of a multimodal fusion technique which exploits the benefits of a Bayesian inference scheme to enhance surveillance systems’ reliability. Additionally, an automatic object classifier is proposed based on the multimodal fusion technique, addressing semantic indexing and classification for forensic applications. The proposed Bayesian-based Multimodal Fusion technique, and particularly, the proposed object classifier are evaluated against two state-of-the-art automatic object classifiers on the i-LIDS surveillance dataset.

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References

  1. 1.
    Fernandez Arguedas, V., Zhang, Q., Chandramouli, K., Izquierdo, E.: Vision Based Semantic Analysis of Surveillance Videos. In: Anagnostopoulos, I.E., Bieliková, M., Mylonas, P., Tsapatsoulis, N. (eds.) Semantic Hyper/Multi-media Adaptation. SCI, vol. 418, pp. 83–126. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Atrey, P., Hossain, M., El Saddik, A., Kankanhalli, M.: Multimodal fusion for multimedia analysis: a survey. Multimedia Systems 16, 345–379 (2010)CrossRefGoogle Scholar
  3. 3.
    Snoek, C., Worring, M., Smeulders, A.: Early versus late fusion in semantic video analysis. In: ACM Multimedia (2005)Google Scholar
  4. 4.
    Wu, Z., Cai, L., Meng, H.: Multi-level Fusion of Audio and Visual Features for Speaker Identification. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 493–499. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Zhang, Q., Izquierdo, E.: Combining low-level features for semantic inference in image retrieval. EURASIP Journal on Advances in Signal Processing 12 (2007)Google Scholar
  6. 6.
    Jaffre, G., Pinquier, J.: Autdio/video fusion: a preprocessing step for multimodal person identification. In: MMUA (2006)Google Scholar
  7. 7.
    Kankanhalli, M., Wang, J., Jain, R.: Experiential sampling in multimedia systems. IEEE Transactions on Multimedia 8, 937–946 (2006)CrossRefGoogle Scholar
  8. 8.
    Nirmala, D., Paul, B., Vaidehi, V.: A novel multimodal image fusion method using shift invariant discrete wavelet transform and support vector machines. In: ICRTIT, pp. 932–937 (2011)Google Scholar
  9. 9.
    Arsic, D., Schuller, B., Rigoll, G.: Suspicious behavior detection in public transport by fusion of low-level video descriptors. In: ICME, pp. 2018–2021 (2007)Google Scholar
  10. 10.
    Bahlmann, C., Zhu, Y., Ramesh, V., Pellkofer, M., Koehler, T.: A system for traffic sign detection, tracking, and recognition using color, shape, and motion information. In: IEEE Intelligent Vehicles Symposium, pp. 255–260. IEEE (2005)Google Scholar
  11. 11.
    Meuter, M., Nunn, C., Görmer, S., Müller-Schneiders, S., Kummert, A.: A decision fusion and reasoning module for a traffic sign recognition system. IEEE Transactions on Intelligent Transportation Systems, 1–9 (2011)Google Scholar
  12. 12.
    Klausner, A., Tengg, A., Rinner, B.: Vehicle classification on multi-sensor smart cameras using feature-and decision-fusion. In: ICDSC, pp. 67–74. IEEE (2007)Google Scholar
  13. 13.
    Xiao, J., Wang, X.: Study on traffic flow prediction using rbf neural network. In: ICMLC, vol. 5, pp. 2672–2675 (2004)Google Scholar
  14. 14.
    Ozkurt, C., Camci, F.: Automatic traffic density estimation and vehicle classification for traffic surveillance systems using neural networks. Mathematical and Computational Applications 14, 187 (2010)Google Scholar
  15. 15.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  16. 16.
    Paisitkriangkrai, S., Shen, C., Zhang, J.: Performance evaluation of local features in human classification and detection. IET Computer Vision 2, 236–246 (2008)CrossRefGoogle Scholar
  17. 17.
    Chen, X., Zhang, C.: Vehicle Classification from Traffic Surveillance Videos at a Finer Granularity. In: Cham, T.-J., Cai, J., Dorai, C., Rajan, D., Chua, T.-S., Chia, L.-T. (eds.) MMM 2007. LNCS, vol. 4351, pp. 772–781. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Thi, T., Robert, K., Lu, S., Zhang, J.: Vehicle classification at nighttime using eigenspaces and support vector machine. In: ICISP, vol. 2, pp. 422–426. IEEE (2008)Google Scholar
  19. 19.
    Kafai, M., Bhanu, B.: Dynamic bayesian networks for vehicle classification in video. IEEE Transactions on Industrial Informatics, 1 (2012)Google Scholar
  20. 20.
    Cho, W., Kim, S., Ahn, G.: Detection and recognition of moving objects using the temporal difference method and the hidden markov model. In: CSAE, vol. 4, pp. 119–123 (2011)Google Scholar
  21. 21.
    Zhang, Z., Li, M., Huang, K., Tan, T.: Boosting local feature descriptors for automatic objects classification in traffic scene surveillance. In: ICPR, pp. 1–4 (2008)Google Scholar
  22. 22.
    Gurwicz, Y., Yehezkel, R., Lachover, B.: Multiclass object classification for real-time video surveillance systems. Pattern Recognition Letters (2011)Google Scholar
  23. 23.
    Fernandez Arguedas, V., Zhang, Q., Chandramouli, K., Izquierdo, E.: Multi-feature fusion for surveillance video indexing. In: WIAMIS. IEEE (2011)Google Scholar
  24. 24.
    Fernandez Arguedas, V., Izquierdo, E.: Object classification based on behaviour patterns. In: ICDP (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Virginia Fernandez Arguedas
    • 1
  • Qianni Zhang
    • 1
  • Ebroul Izquierdo
    • 1
  1. 1.Multimedia and Vision Research Group, School of Electronic Engineering and Computer ScienceQueen Mary, University of LondonLondonUK

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