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Spatial Recurrences for Pedestrian Classification

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Abstract

In this work, a framework is proposed for pedestrian classification based on spatial recurrences in the form of recurrence plots. This representation is more general and potentially more discriminative than a histogram of co-occurrences, since the correlation information between different spatial locations is maintained rather than just summarized into histograms. Recurrences are defined over “states” that encode some visual information at spatial locations over a grid. The framework is general in that it accommodates states of arbitrary nature, any similarity distance between pairs of states, arbitrary grids, and varying degree of recurrence summarization. As revealed experimentally, the resulting descriptor is competitive to recent approaches and compares favourably to an state-of-the-art co-occurrence-based descriptor under several occlusion conditions, and in related problems such as pedestrian view recognition, in particular under stringent quantization conditions. One interesting additional finding is that splitting the recurrent information into multiple recurrence plots turns out to be more discriminative than condensing it into fewer plots.

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References

  1. Daimler pedestrian benchmarks website: http://www.science.uva.nl/research/isla/downloads/pedestrians/. Accessed 24 June 2012

  2. Andriluka, M., Roth, S., Schiele, B.: Pictorial structures revisited: people detection and articulated pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), pp. 1014–1021 (2009)

    Chapter  Google Scholar 

  3. Bourdev, L., Maji, S., Malik, J.: Describing people: a poselet-based approach to attribute classification. In: IEEE 13th International Conference on Computer Vision (ICCV 2011) (2011)

    Google Scholar 

  4. Burges, C.J.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2, 121–167 (1998)

    Article  Google Scholar 

  5. Cao, L., Dikmen, M., Fu, Y., Huang, T.: Gender labels on the CBCL pedestrian image database. http://www.ifp.illinois.edu/~cao4/data/gender_pedestrian_mm08.zip. Accessed 24 June 2012

  6. Cao, L., Dikmen, M., Fu, Y., Huang, T.: Gender recognition from body. In: Proceedings of the 16th ACM international conference on Multimedia (MM’2008), pp. 725–728. ACM, New York (2008). doi:10.1145/1459359.1459470

    Chapter  Google Scholar 

  7. Center for biological and computational learning (CBCL) at MIT: CBCL pedestrian image database website. http://cbcl.mit.edu/software-datasets/PedestrianData.html. Accessed 24 June 2012

  8. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893 (2005)

    Google Scholar 

  9. Dollár, P., Tu, Z., Tao, H., Belongie, S.: Feature mining for image classification. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), pp. 1–8 (2007)

    Google Scholar 

  10. Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: a benchmark. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), pp. 304–311 (2009)

    Google Scholar 

  11. Eckmann, J.P., Kamphorst, S.O., Ruelle, D.: Recurrence plots of dynamical systems. Europhys. Lett. 4(9), 973–977 (1987)

    Article  Google Scholar 

  12. Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: survey and experiments. IEEE Trans. Pattern Anal. Mach. Intell. 31, 2179–2195 (2009)

    Article  Google Scholar 

  13. Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008). Software available at http://www.csie.ntu.edu.tw/~cjlin/liblinear

    MATH  Google Scholar 

  14. Farhadi, A., Endres, I., Hoiem, D., Forsyth, D.: Describing objects by their attributes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), pp. 1778–1785 (2009)

    Chapter  Google Scholar 

  15. Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27, 861–874 (2006)

    Article  Google Scholar 

  16. Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. Int. J. Comput. Vis. 61, 55–79 (2005)

    Article  Google Scholar 

  17. Gavrila, D., Philomin, V.: Real-time object detection for ‘smart’ vehicles. In: IEEE 7th International Conference on Computer Vision (ICCV 1999), vol. 1, pp. 87–93 (1999)

    Chapter  Google Scholar 

  18. Huang, J., Ling, C.X.: Using AUC and accuracy in evaluating learning algorithms. IEEE Trans. Knowl. Data Eng. 17(3), 299–310 (2005)

    Article  Google Scholar 

  19. Ito, S., Kubota, S.: Object classification using heterogeneous co-occurrence features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) 11th European Conference on Computer Vision (ECCV 2010). Lecture Notes in Computer Science, vol. 6312, pp. 209–222. Springer, Berlin (2010)

    Chapter  Google Scholar 

  20. Kozakaya, T., Ito, S., Kubota, S., Yamaguchi, O.: Cat face detection with two heterogeneous features. In: IEEE 16th International Conference on Image Processing (ICIP 2009), pp. 1213–1216 (2009)

    Chapter  Google Scholar 

  21. Maji, S., Berg, A., Malik, J.: Classification using intersection kernel support vector machines is efficient. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), pp. 1–8 (2008)

    Google Scholar 

  22. Marwan, N., Kurths, J., Saparin, P.: Generalised recurrence plot analysis for spatial data. Phys. Lett. A 360(4–5), 545–551 (2007)

    Article  Google Scholar 

  23. Marwan, N., Romano, M.C., Thiel, M., Kurths, J.: Recurrence plots for the analysis of complex systems. Phys. Rep. 438(5–6), 237–329 (2007)

    Article  MathSciNet  Google Scholar 

  24. Mocenni, C., Facchini, A., Vicino, A.: Identifying the dynamics of complex spatio-temporal systems by spatial recurrence properties. Proc. Natl. Acad. Sci. USA 107(18), 8097–8102 (2010)

    Article  Google Scholar 

  25. Munder, S., Gavrila, D.: An experimental study on pedestrian classification. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1863–1868 (2006)

    Article  Google Scholar 

  26. Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., Poggio, T.: Pedestrian detection using wavelet templates. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 1997), pp. 193–199 (1997). doi:10.1109/CVPR.1997.609319

    Chapter  Google Scholar 

  27. Sabzmeydani, P., Mori, G.: Detecting pedestrians by learning shapelet features. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), pp. 1–8. IEEE Computer Society, Los Alamitos (2007)

    Google Scholar 

  28. Serra-Toro, C., Traver, V.J.: A new pedestrian detection descriptor based on the use of spatial recurrences. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, vol. 6855, pp. 97–104. Springer, Berlin (2011)

    Chapter  Google Scholar 

  29. Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)

    MATH  Google Scholar 

  30. Walk, S., Majer, N., Schindler, K., Schiele, B.: New features and insights for pedestrian detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010), pp. 1030–1037 (2010)

    Google Scholar 

  31. Watanabe, T., Ito, S., Yokoi, K.: Co-occurrence histograms of oriented gradients for pedestrian detection. In: Wada, T., Huang, F., Lin, S. (eds.) Advances in Image and Video Technology. Lecture Notes in Computer Science, vol. 5414, pp. 37–47. Springer, Berlin (2009)

    Chapter  Google Scholar 

  32. Yuan, J., Yang, M., Wu, Y.: Mining discriminative co-occurrence patterns for visual recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2011), pp. 2777–2784 (2011)

    Google Scholar 

  33. Zhang, W., Zelinsky, G., Samaras, D.: Real-time accurate object detection using multiple resolutions. In: IEEE 11th International Conference on Computer Vision (ICCV 2007), pp. 1–8 (2007)

    Google Scholar 

  34. Zhu, Q., Yeh, M.C., Cheng, K.T., Avidan, S.: Fast human detection using a cascade of histograms of oriented gradients. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 2, pp. 1491–1498 (2006)

    Google Scholar 

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Acknowledgements

The authors acknowledge the Spanish research programme Consolider Ingenio-2010 CSD2007-00018, and Fundació Caixa-Castelló Bancaixa under project P1⋅1A2010-11. Carlos Serra-Toro is funded by Generalitat Valenciana under the “VALi+d program for research personnel in training” with grant code ACIF/2010/135. Part of this work was done while he was in a research stay funded by Generalitat Valenciana with grant code BEFPI/2012/030.

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Correspondence to Carlos Serra-Toro.

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Serra-Toro, C., Traver, V.J. & Montoliu, R. Spatial Recurrences for Pedestrian Classification. J Math Imaging Vis 47, 108–123 (2013). https://doi.org/10.1007/s10851-012-0382-7

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