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Convolutional Neural Networks with Neural Cascade Classifier for Pedestrian Detection

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Pattern Recognition (CCPR 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 662))

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Abstract

The combination of traditional methods (e.g., ACF) and Convolutional Neural Networks (CNNs) has achieved great success in pedestrian detection. Despite effectiveness, design of this method is intricate. In this paper, we present an end-to-end network based on Faster R-CNN and neural cascade classifier for pedestrian detection. Different from Faster R-CNN that only makes use of the last convolutional layer, we utilize features from multiple layers and feed them to a neural cascade classifier. Such an architecture favors more low-level features and implements a hard negative mining process in the network. Both of these two factors are important in pedestrian detection. The neural cascade classifier is jointly trained with the Faster R-CNN in our unifying network. The proposed network achieves comparable performance to the state-of-the-art on Caltech pedestrian dataset with a more concise framework and faster processing speed. Meanwhile, the detection result obtained by our method is tighter and more accurate.

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References

  1. Alexe, B., Deselaers, T., Ferrari, V.: Measuring the objectness of image windows. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2189–2202 (2012)

    Article  Google Scholar 

  2. Benenson, R., Mathias, M., Timofte, R., Van Gool, L.: Fast stixel computation for fast pedestrian detection. In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012 Ws/Demos, Part III. LNCS, vol. 7585, pp. 11–20. Springer, Heidelberg (2012)

    Google Scholar 

  3. Benenson, R., Mathias, M., Timofte, R., Van Gool, L.: Pedestrian detection at 100 frames per second. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2903–2910. IEEE (2012)

    Google Scholar 

  4. Benenson, R., Mathias, M., Tuytelaars, T., Gool, L.: Seeking the strongest rigid detector. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3666–3673 (2013)

    Google Scholar 

  5. Benenson, R., Omran, M., Hosang, J., Schiele, B.: Ten years of pedestrian detection, what have we learned? In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014 Workshops. LNCS, vol. 8926, pp. 613–627. Springer, Heidelberg (2015)

    Google Scholar 

  6. Cai, Z., Saberian, M., Vasconcelos, N.: Learning complexity-aware cascades for deep pedestrian detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3361–3369 (2015)

    Google Scholar 

  7. Cao, J., Pang, Y., Li, X.: Learning multilayer channel features for pedestrian detection (2016). arXiv preprint: arXiv:1603.00124

  8. Carreira, J., Sminchisescu, C.: CPMC: automatic object segmentation using constrained parametric min-cuts. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1312–1328 (2012)

    Article  Google Scholar 

  9. Cheng, M.M., Zhang, Z., Lin, W.Y., Torr, P.: BING: binarized normed gradients for objectness estimation at 300fps. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3286–3293 (2014)

    Google Scholar 

  10. 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 

  11. Dollár, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1532–1545 (2014)

    Article  Google Scholar 

  12. 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)

    Chapter  Google Scholar 

  13. Dollár, P., Belongie, S., Perona, P.: The fastest pedestrian detector in the west. In: BMVC, vol. 2, p. 7. Citeseer (2010)

    Google Scholar 

  14. Dollár, P., Tu, Z., Perona, P., Belongie, S.J.: Integral channel features. In: BMVC, pp. 1–11. British Machine Vision Association (2009)

    Google Scholar 

  15. Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34(4), 743–761 (2012)

    Article  Google Scholar 

  16. Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)

    Google Scholar 

  17. Felzenszwalb, P.F., Girshick, R.B., McAllester, D.: Cascade object detection with deformable part models. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2241–2248. IEEE (2010)

    Google Scholar 

  18. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)

    Article  Google Scholar 

  19. Luo, P., Tian, Y., Wang, X., Tang, X.: Switchable deep network for pedestrian detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 899–906 (2014)

    Google Scholar 

  20. Nam, W., Dollár, P., Han, J.H.: Local decorrelation for improved pedestrian detection. In: Advances in Neural Information Processing Systems, pp. 424–432 (2014)

    Google Scholar 

  21. Ouyang, W., Wang, X.: Joint deep learning for pedestrian detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2056–2063 (2013)

    Google Scholar 

  22. Ouyang, W., Wang, X., Zeng, X., Qiu, S., Luo, P., Tian, Y., Li, H., Yang, S., Wang, Z., Loy, C.C., et al.: Deepid-net: deformable deep convolutional neural networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2403–2412 (2015)

    Google Scholar 

  23. Park, D., Zitnick, C., Ramanan, D., Dollár, P.: Exploring weak stabilization for motion feature extraction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2882–2889 (2013)

    Google Scholar 

  24. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)

    Google Scholar 

  25. Sermanet, P., Kavukcuoglu, K., Chintala, S., LeCun, Y.: Pedestrian detection with unsupervised multi-stage feature learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3626–3633 (2013)

    Google Scholar 

  26. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint: arXiv:1409.1556

  27. Tian, Y., Luo, P., Wang, X., Tang, X.: Deep learning strong parts for pedestrian detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1904–1912 (2015)

    Google Scholar 

  28. Tian, Y., Luo, P., Wang, X., Tang, X.: Pedestrian detection aided by deep learning semantic tasks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5079–5087 (2015)

    Google Scholar 

  29. Uijlings, J.R., van de Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. Int. J. Comput. Vis. 104(2), 154–171 (2013)

    Article  Google Scholar 

  30. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)

    Article  Google Scholar 

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

    Google Scholar 

  32. Yang, B., Yan, J., Lei, Z., Li, S.Z.: Convolutional channel features. In: 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, 7–13 December, pp. 82–90 (2015)

    Google Scholar 

  33. Yang, Y., Wang, Z., Wu, F.: Exploring prior knowledge for pedestrian detection. In: Proceedings of the British Machine Vision Conference, BMVC 2015, Swansea, UK, 7–10 September, pp. 176.1–176.12 (2015)

    Google Scholar 

  34. Zhang, S., Bauckhage, C., Cremers, A.: Informed haar-like features improve pedestrian detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 947–954 (2014)

    Google Scholar 

  35. Zhang, S., Benenson, R., Omran, M., Hosang, J., Schiele, B.: How far are we from solving pedestrian detection? (2016). arXiv preprint: arXiv:1602.01237

  36. Zhang, S., Benenson, R., Schiele, B.: Filtered feature channels for pedestrian detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1751–1760 (2015)

    Google Scholar 

  37. Zitnick, C.L., Dollár, P.: Edge boxes: locating object proposals from edges. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 391–405. Springer, Heidelberg (2014)

    Google Scholar 

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Acknowledgement

This work was supported in part by the Projects of the National Natural Science Foundation of China (Grant No. 61375043, 61403375, 61272394) and the Beijing Natural Science Foundation (Grant No. 4142057).

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Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Bei Tong, Bin Fan & Fuchao Wu

Authors
  1. Bei Tong
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  2. Bin Fan
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  3. Fuchao Wu
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Corresponding author

Correspondence to Bei Tong .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  2. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China

    Xuelong Li

  3. Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

    Xilin Chen

  4. Tsinghua University , Beijing, China

    Jie Zhou

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  6. University of Electronic Science and Technology, Chengdu, Sichuan, China

    Hong Cheng

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© 2016 Springer Nature Singapore Pte Ltd.

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Tong, B., Fan, B., Wu, F. (2016). Convolutional Neural Networks with Neural Cascade Classifier for Pedestrian Detection. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_21

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  • DOI: https://doi.org/10.1007/978-981-10-3002-4_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3001-7

  • Online ISBN: 978-981-10-3002-4

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