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.
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References
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)
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)
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)
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)
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)
Dollár, P., Belongie, S., Perona, P.: The fastest pedestrian detector in the west. In: BMVC, vol. 2, p. 7 (2010)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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Wang, X. et al. (2014). Saliency-Based Deformable Model for Pedestrian Detection. In: Ooi, W.T., Snoek, C.G.M., Tan, H.K., Ho, CK., Huet, B., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2014. PCM 2014. Lecture Notes in Computer Science, vol 8879. Springer, Cham. https://doi.org/10.1007/978-3-319-13168-9_21
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DOI: https://doi.org/10.1007/978-3-319-13168-9_21
Publisher Name: Springer, Cham
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