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Fast Pedestrian Detection Based on the Selective Window Differential Filter

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Abstract

Following the recent progress of the pixel-level filtering for pedestrian detection, we propose a window differential feature (WDF) based on the multiple channel maps. More specifically, WDF encodes first-order statistics between artitary two pixels in the whole detection window, thus obtaining larger receptive field for achor pixel than other filtering methods. Despite obtaining more discriminative information for pedestrian, WDF suffers expensive space complexity due to the high feature dimensionality. Quantitive analysis for the arbitrary pairwise elements in the WDF vector demonstrates the weak correlations existing in the proposed feature, thus motivate dimension reduction with feature selection to be the top choice. Three different dimension reduction methods for the WDF demonstrate that feature selection with mutual information achieves superior result. In addition, we find the complementary characteristics between the baseline feature and selective window differential feature, thus combining both can obtain further performance improvement. Extensive experiments on the INRIA, Caltech, ETH, and TUD-Brussel datasets consistently show superior performance of the proposed method to state-of-the-art methods with a 22 fps running speed for 640 \(\times \) 480 images.

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Acknowledgements

This project is supported by the NSF of Jiangsu Province (Grants Nos. BK20140566, BK20150470), the NSF of China (61305058, 61603080), the Fundamental Research Funds for the Jiangsu University (13JDG093), the NSF of the Jiangsu Higher Education Institutes of China (15KJB520008, 16KJB520009).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu, China

    Xin Zuo, Hualong Yu & Dan Xu

  2. School of Electronic and Informatics Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China

    Jifeng Shen

  3. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, Jiangsu, China

    Chengshan Qian

  4. School of Civil and Environmental Engineering, Oklahoma State University, Stillwater, OK, 74074, USA

    Yongwei Shan

Authors
  1. Xin Zuo
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  2. Jifeng Shen
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  3. Hualong Yu
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  4. Dan Xu
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  5. Chengshan Qian
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  6. Yongwei Shan
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Correspondence to Jifeng Shen.

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Cite this article

Zuo, X., Shen, J., Yu, H. et al. Fast Pedestrian Detection Based on the Selective Window Differential Filter. Neural Process Lett 48, 403–417 (2018). https://doi.org/10.1007/s11063-017-9746-8

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