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Efficient human detection in crowded environment

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Abstract

Detecting humans in crowded environment is profitable but challenging in video surveillance. We propose an efficient human detection method by combining both motion and appearance clues. Moving pixels are first extracted by background subtraction, and then a filtering step is used to narrow the range for human template matching. We utilize integral images to fast generate shape information from edge maps of each frame and define the matching probability to be capable of detecting both full-body and partial-body. Representative human templates are constructed by sparse contours on the basis of the point distribution model. Moreover, linear regression analysis is also applied to adaptively adjust the template sizes. With the aid of the proposed foreground ratio filtering and the multi-sized template matching techniques, experimental results show that our method not only can efficiently detect humans in a crowded environment, but also largely enhance the resultant detection accuracy.

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References

  1. Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.P.: Pfinder: real-time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 780–785 (1997)

    Article  Google Scholar 

  2. Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 246–252 (1999)

  3. Hachaj, T., Ogiela, M.R.: Rule-based approach to recognizing human body poses and gestures in real time. Multimedia Syst. 1–19 (2013)

  4. Zheng, G., Chen, Y.: A review on vision-based pedestrian detection. In: IEEE Global High Tech Congress on Electronics, pp. 49–54 (2012)

  5. Li, B., Yao, Q., Wang, K.: A review on vision-based pedestrian detection in intelligent transportation systems. In: Proceedings of the IEEE International Conference on Networking, Sensing and Control, pp. 393–398 (2012)

  6. Wu, B., Nevatia, R.: Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 90–97 (2005)

  7. Opelt, A., Zisserman, A.: A boundary-fragment-model for object detection. In: Proceedings of the European Conference on Computer Vision, pp. 575–588 (2006)

  8. Ferrari, V., Tuytelaars, T., Gool, L.V.: Object detection by contour segment networks. In: Proceedings of the European Conference on Computer Vision (2006)

  9. Papageorgiou, C., Poggio, T.: A trainable system for object detection. IInt. J. Comput. Vis. 38(1), 15–33 (2000)

    Article  MATH  Google Scholar 

  10. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)

  11. Gavrila, D.M., Giebel, J.: Shape-based pedestrian detection and tracking. In: Proceedings of the IEEE Intelligent Vehicle Symposium, pp. 8–14 (2002)

  12. Toth, D., Aach, T.: Detection and recognition of moving objects using statistical motion detection and fourier descriptors. In: Proceedings of the International Conference on Image Analysis and Processing (2003)

  13. Zhou, J., Hoang, J.: Real time robust human detection and tracking system. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 149–149 (2005)

  14. Rittscher, J., Tu, P.H., Krahnstoever, N.: Simultaneous estimation of segmentation and shape. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 486–493 (2005)

  15. Zhao, T., Nevatia, R.: Tracking multiple humans in crowded environment. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 406–413 (2004)

  16. Beleznai, C., Bischof, H.: Fast human detection in crowded scenes by contour integration and local shape estimation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2246–2253 (2009)

  17. Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape modelstheir training and application. Comput. Vis. Image Underst. 61(1), 3859 (1995)

    Article  Google Scholar 

  18. Kim, K., Chalidabhongse, T., Harwood, D., Davis, L.: Real-time foregroundbackground segmentation using codebook model. Real-Time Imaging Spec. Issue Video Object Process. 11(3), 172–185 (2005)

    Google Scholar 

  19. Derpanis, K.G.: York University, memo. Integral image-based representations (2007)

  20. Crow, F.C.: Summed-area tables for texture mapping. In: Proceedings of the International Conference on Computer Graphics and Interactive Techniques (1984)

  21. Simard, P.Y., Bottou, L., Haffner, P., LeCun, Y.: Boxlets: a fast convolution algorithm for signal processing and neural networks. In: Kearns, M., Solla, S., Cohn, D. (eds.) Advances in neural information, p. 571577. MIT press, Cambridge (1999)

    Google Scholar 

  22. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 511–518 (2001)

  23. Tuzel, O., Porikli, F., Meer, P.: Region covariance: a fast descriptor for detection and classication. In: Proceedings of the European Conference on Computer Vision, pp. 589600 (2006)

  24. Wang, X., Doretto, G., Sebastian, T., Rittscher, J., Tu, P.: Shape and appearance context modeling. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1–8 (2007)

  25. Movellan, J.R.: Tutorial on gabor filters. http://mplab.ucsd.edu/tutorials/gabor

  26. Caviar dataset. http://homepages.inf.ed.ac.uk/rbf/CAVIAR/

  27. Lin, Z., Davis, L.S., Doermann, D., DeMenthon, D.: Hierarchical part-template matching for human detection and segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1–8 (2007)

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, ROC

    Min-Chun Hu & Jhe-Wei Li

  2. Research Center for Information Technology Innovation (CITI), Academia Sinica, Taipei, Taiwan, ROC

    Wen-Huang Cheng

  3. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, ROC

    Chuan-Shen Hu & Ja-Ling Wu

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  1. Min-Chun Hu
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  2. Wen-Huang Cheng
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  3. Chuan-Shen Hu
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  4. Ja-Ling Wu
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  5. Jhe-Wei Li
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Correspondence to Wen-Huang Cheng.

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Hu, MC., Cheng, WH., Hu, CS. et al. Efficient human detection in crowded environment. Multimedia Systems 21, 177–187 (2015). https://doi.org/10.1007/s00530-014-0391-z

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