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2018 | OriginalPaper | Chapter

Pedestrian Detection Based on Visual Saliency and Supervised Learning

Authors : Wanhan Zhang, Jie Ren, Meihua Gu

Published in: Advances in Brain Inspired Cognitive Systems

Publisher: Springer International Publishing

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Abstract

Pedestrian detection is a key issue in computer vision, which received extensive attentions. Supervised learning methods with feature extraction and classification are widely used in the pedestrian detection. This paper proposed a pedestrian detection method based on visual saliency and supervised learning. The LC algorithm is used to calculate the saliency value of each training image, followed by the LBP feature extraction. The saliency LBP features and HOG features are combined together as the input of SVM classifier to detect pedestrians. Experimental results show that this method is more accurate and efficient compared with the traditional HOG and LBP feature fusion based method.

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Literature
1.
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Dalal, N., Triggs, B. (eds.) Proceedings 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, pp. 886–893. IEEE Computer Society, San Diego (2005)
2.
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRef
3.
Mu, Y., Yan, S., Liu, Y., et al.: Discriminative local binary patterns for human detection in personal album. In: Mu, Y., Yan, S., Liu, Y., et al. (eds.) 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1–8. IEEE Computer Society, Anchorage (2008)
4.
Wu, J., Rehg, M.: A visual descriptor for scene categorization. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1489–1501 (2011)CrossRef
5.
Wu, J., Geyer, C., Rehg, M.: Real-time human detection using contour cues. In: 2011 IEEE International Conference on Robotics and Automation, ICRA 2011, pp. 860–867. Institute of Electrical and Electronics Engineers Inc., Shanghai (2011)
6.
Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. Int. J. Comput. Vis. 63(2), 153–161 (2003)CrossRef
7.
Wang, X.: An HOG-LBP human detector with partial occlusion handling. In: Computer Vision, Kyoto Japan, vol. 30, pp. 32–39 (2009)
8.
Walk, S., Majer, N., Schindler, K., et al.: New features and insights for pedestrian detection. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010, pp. 1030–1037. IEEE Computer Society, San Francisco (2010)
9.
Tuzel, O., Porikli, F., Meer, P.: Pedestrian detection via classification on Riemannian manifolds. IEEE Trans. Pattern Anal. Mach. Intell. 30(10), 1713–1727 (2008)CrossRef
10.
Wantanbe, T., Ito, S., Yokoi K.: Co-occurrence histograms of oriented gradients for pedestrian detection. In: Advances in Image and Video Technology - Third Pacific Rim Symposium, PSIVT 2009, Proceedings, pp. 37–47. Springer, Tokyo (2009)
11.
Zheng, C.H., Pei, W.J., Yan, Q., Chong, Y.W.: Pedestrian detection based on gradient and texture feature integration. Neurocomputing 228, 71–78 (2017)CrossRef
12.
Du, X., El-Khamy, M., Lee, J., Davis, L.: Fused DNN: a deep neural network fusion approach to fast and robust pedestrian detection. In: Proceedings 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017, pp. 953–961. Institute of Electrical and Electronics Engineers Inc, Santa Rosa (2017)
13.
Ouyang, W., Zhou, H., Li, H., Li, Q., Yan, J., Wang, X.: Jointly learning deep features, deformable parts, occlusion and classification for pedestrian detection. IEEE Trans. Pattern Anal. Mach. Intell. 40(8), 1874–1887 (2017)CrossRef
14.
Zheng, J., Yanan, L., Jinchang, R., Tingge, Z., Yijun, Y.: Fusion of block and keypoints based approaches for effective copy-move image forgery detection. Multidimens. Syst. Signal Process. 27(4), 989–1005 (2016)MathSciNetCrossRef
15.
Yan, Y., et al.: Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention based refinement. Pattern Recognit. 79, 65–78 (2018)CrossRef
16.
Ren, J., Jiang, J., Wang, D., Ipson, S.S.: Fusion of intensity and inter-component chromatic difference for effective and robust colour edge detection. IET Image Process. 4(4), 294–301 (2010)CrossRef
17.
Zhai, Y., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. In: Proceedings of the 14th Annual ACM International Conference on Multimedia, MM 2006, pp. 815–824. Association for Computing Machinery, Santa Barbara (2006)
Metadata
Title
Pedestrian Detection Based on Visual Saliency and Supervised Learning
Authors
Wanhan Zhang
Jie Ren
Meihua Gu
Copyright Year
2018
Book
Advances in Brain Inspired Cognitive Systems

Print ISBN: 978-3-030-00562-7

Electronic ISBN: 978-3-030-00563-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00563-4_49

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