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A Simple Visual Words Selection Strategy for Pedestrian Detection

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Advances in Visual Computing (ISVC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8887))

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Abstract

An effective and efficient visual word selection method based on Bag-of-Features(BoF),which can be applied to the pedestrian detection problem in a single image, is proposed in this paper. We first calculate the difference in the total appearance frequency of each visual word in pedestrian and non-pedestrian images. Visual words that exhibit greater absolute values are more efficient for pedestrian detection, and are thus selected. The effectiveness of the proposed method is validated by analyzing the distribution of selected feature points. Through this analysis, we find that discriminative feature points for pedestrian images are mainly located about the lower body, whereas those for non-pedestrian images are mainly located in background areas. In addition, the experiments show that the time required for detection can be reduced by approximately 50%, with negligible loss in detection accuracy, using the proposed method, even if only 40% of the visual words are selected.

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References

  1. Yan, J., Zhang, X., Lei, Z.: Robust multi-resolution pedestrian detection in traffic scenes. In: IEEE Conf. Comput. Vis. Pattern Recognit, pp. 3033–3040 (2013)

    Google Scholar 

  2. Dollar, P., Wojek, C.: Pedestrian detection: An evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell., 743–761 (2012)

    Google Scholar 

  3. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 1, 886–893 (2005)

    Google Scholar 

  4. Wu, B., Nevatia, R.: Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors. Int. J. Comput. Vis. 75, 247–266 (2007)

    Article  Google Scholar 

  5. Sabzmeydani, P., Mori, G.: Detecting Pedestrians by Learning Shapelet Features. In: 2007 IEEE Conf. Comput. Vis. Pattern Recognit., pp. 1–8 (2007)

    Google Scholar 

  6. Viola, P., Jones, M.J.: Detecting pedestrians using patterns of motion and appearance. In: Proc. Ninth IEEE Int. Conf. Comput. Vis., vol. 2, pp. 734–741 (2003)

    Google Scholar 

  7. Sivic, J., Zisserman, A.: Video Google: a text retrieval approach to object matching in videos. In: Proc. Ninth IEEE Int. Conf. Comput. Vis., vol. 2, pp. 1470–1477 (2003)

    Google Scholar 

  8. Uijlings, J.: Real-time visual concept classification. IEEE Trans. Multimed. 12, 665–681 (2010)

    Article  Google Scholar 

  9. Csurka, G., Dance, C.: Visual categorization with bags of keypoints. In: Proc. Eur. Conf. Comput. Vis., pp. 59–74 (2004)

    Google Scholar 

  10. Winn, J., Criminisi, A., Minka, T.: Object categorization by learned universal visual dictionary. In: IEEE Int. Conf. Comput. Vis., vol. 2, pp. 1800–1807 (2005)

    Google Scholar 

  11. Wang, L., Zhou, L., Shen, C.: A fast algorithm for creating a compact and discriminative visual codebook. In: Procedings Eur. Conf. Comput. Vis., pp. 719–732 (2008)

    Google Scholar 

  12. Moosmann, F., Triggs, B., Jurie, F.: Fast discriminative visual codebooks using randomized clustering forests. Procedings Adv. Neural Inf. Process. Syst., 985–992 (2007)

    Google Scholar 

  13. Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)

    Article  Google Scholar 

  14. Bay, H., Tuytelaars, T., Gool, L.: Van: Surf: Speeded up robust features. In: Eur. Conf. Comput. Vis., pp. 404–417 (2006)

    Google Scholar 

  15. Enzweiler, M., Member, S., Gavrila, D.M.: Monocular Pedestrian Detection: Survey and Experiments 31, 2179–2195 (2009)

    Google Scholar 

  16. Cao, H., Naito, T., Ninomiya, Y.: Approximate RBF kernel SVM and its applications in pedestrian classification. In: Int. Work. Mach. Learn. Vision-based Motion Anal. (2008)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Graduate School of Systems Science and Technology, Akita Prefectural University, Japan

    Xingguo Zhang

  2. Faculty of Systems Science and Technology, Akita Prefectural University, Japan

    Guoyue Chen, Kazuki Saruta & Yuki Terata

Authors
  1. Xingguo Zhang
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  2. Guoyue Chen
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  3. Kazuki Saruta
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  4. Yuki Terata
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada at Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. The University of Texas at Dallas, 75080, Richardson, TX, USA

    Ryan McMahan

  6. NextGen Interactions, 27604, Raleigh, NC, USA

    Jason Jerald

  7. Indiana University, 46202, Indianapolis, IN, USA

    Hui Zhang

  8. Microsoft Research, 1 Microsoft Way, 98052, Redmond, WA, USA

    Steven M. Drucker

  9. University of Delaware, 19716-2712, Newark, DE, USA

    Chandra Kambhamettu

  10. Intel Corp., 95054, Santa Clara, CA, USA

    Maha El Choubassi

  11. Computer Graphics and Interactive Media Lab, Department of Computer Science, University of Houston, 77004, Houston, TX, USA

    Zhigang Deng

  12. NVIDIA, 34788, Leesburg, FL, USA

    Mark Carlson

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© 2014 Springer International Publishing Switzerland

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Zhang, X., Chen, G., Saruta, K., Terata, Y. (2014). A Simple Visual Words Selection Strategy for Pedestrian Detection. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8887. Springer, Cham. https://doi.org/10.1007/978-3-319-14249-4_63

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  • DOI: https://doi.org/10.1007/978-3-319-14249-4_63

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14248-7

  • Online ISBN: 978-3-319-14249-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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