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Saliency-Based Deformable Model for Pedestrian Detection

  • Xiao Wang
  • Jun Chen
  • Wenhua Fang
  • Chao Liang
  • Chunjie Zhang
  • Kaimin Sun
  • Ruimin Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8879)

Abstract

Pedestrian detection, which is to identify category (pedestrian) of object and give the position information in the image, is an important and yet challenging task due to the intra-class variation of pedestrians in clothing and articulation. Previous researches mainly focus on feature extraction and sliding window, where the former aims to find robust feature representation while the latter seeks to locate the latent position. However, most of sliding windows are based on scale transformation and traverse the entire image. Therefore, it will bring computational complexity and false detection which is not necessary. To conquer the above difficulties, we propose a novel Saliency-Based Deformable Model (SBDM) method for pedestrian detection. In SBDM method we present that, besides the local features, the saliency in the image provides important constraints that are not yet well utilized. And a probabilistic framework is proposed to model the relationship between Saliency detection and the feature (Deformable Model) via a Bayesian rule to detect pedestrians in the still image.

Keywords

Pedestrian detection Saliency-Based Deformable Model Saliency Detection Bayesian rule 

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References

  1. 1.
    Azizpour, H., Laptev, I.: Object detection using strongly-supervised deformable part models. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 836–849. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Cheng, M.M., Zhang, G.X., Mitra, N.J., Huang, X., Hu, S.M.: Global contrast based salient region detection. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 409–416. IEEE (2011)Google Scholar
  3. 3.
    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. IEEE (2005)Google Scholar
  4. 4.
    Ding, Y., Xiao, J.: Contextual boost for pedestrian detection. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2895–2902. IEEE (2012)Google Scholar
  5. 5.
    Dollár, P., Appel, R., Kienzle, W.: Crosstalk cascades for frame-rate pedestrian detection. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 645–659. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Dollár, P., Belongie, S., Perona, P.: The fastest pedestrian detector in the west. In: BMVC, vol. 2, p. 7 (2010)Google Scholar
  7. 7.
    Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: An evaluation of the state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(4), 743–761 (2012)CrossRefGoogle Scholar
  8. 8.
    Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2360–2367. IEEE (2010)Google Scholar
  9. 9.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  10. 10.
    Johnson, S., Everingham, M.: Learning effective human pose estimation from inaccurate annotation. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1465–1472. IEEE (2011)Google Scholar
  11. 11.
    Kostinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2288–2295. IEEE (2012)Google Scholar
  12. 12.
    Ouyang, W., Wang, X.: Single-pedestrian detection aided by multi-pedestrian detection. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3198–3205. IEEE (2013)Google Scholar
  13. 13.
    Ouyang, W., Zeng, X., Wang, X.: Modeling mutual visibility relationship in pedestrian detection. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3222–3229. IEEE (2013)Google Scholar
  14. 14.
    Paisitkriangkrai, S., Shen, C., Hengel, A.V.D.: Efficient pedestrian detection by directly optimizing the partial area under the roc curve. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 1057–1064. IEEE (2013)Google Scholar
  15. 15.
    Yan, J., Lei, Z., Yi, D., Li, S.Z.: Multi-pedestrian detection in crowded scenes: A global view. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3124–3129. IEEE (2012)Google Scholar
  16. 16.
    Yang, Y., Ramanan, D.: Articulated pose estimation with flexible mixtures-of-parts. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1385–1392. IEEE (2011)Google Scholar
  17. 17.
    Zeng, X., Ouyang, W., Wang, X.: Multi-stage contextual deep learning for pedestrian detection. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 121–128. IEEE (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xiao Wang
    • 1
  • Jun Chen
    • 1
    • 2
  • Wenhua Fang
    • 1
  • Chao Liang
    • 1
    • 2
  • Chunjie Zhang
    • 3
  • Kaimin Sun
    • 4
  • Ruimin Hu
    • 1
    • 2
  1. 1.National Engineering Research Center for Multimedia Software, School of ComputerWuhan UniversityWuhanChina
  2. 2.Research Institute of Wuhan University in ShenzhenChina
  3. 3.School of Computer and Control EngineeringUniversity of Chinese Academy of SciencesBeijingChina
  4. 4.School of Remote Sensing Information EngineeringWuhan UniversityWuhanChina

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