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Fusion of Modern and Tradition: A Multi-stage-Based Deep Network Approach for Head Detection

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Book cover Advances in Knowledge Discovery and Data Mining (PAKDD 2018)

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Abstract

Detecting humans in video is becoming essential for monitoring crowd behavior. Head detection is proven as a promising way to realize detecting and tracking crowd. In this paper, a novel learning strategy, called Deep Motion Information Network (abbr. as DMIN) is proposed for head detection. The concept of DMIN is to borrow the traditional well-developed head detection approaches which are composed of multiple stages, and then replace each stages in the pipeline into a cascade of sub-deep-networks to simulate the function of each stage. This learning strategy can lead to many benefits such as preventing many trial and error in designing deep networks, achieving global optimization for each stage, and reducing the amount of training dataset needed. The proposed approach is validated using the PETS2009 dataset. The results show the proposed approach can achieve impressive speedup of the process in addition to significant improvement in recall rates. A very high F-score of 85% is achieved using the proposed network that is by far higher than other methods proposed in literature.

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Acknowledgment

This work was supported by the Ministry of Science and Technology, Taiwan, under Grant MOST 106-2218-E-032-004-MY2.

Author information

Authors and Affiliations

  1. Department of Electric Engineering, University of Melbourne, Melbourne, Australia

    Fu-Chun Hsu

  2. Department of Computer Science and Information Engineering, Tamkang University, Taipei, Taiwan

    Chih-Chieh Hung

Authors
  1. Fu-Chun Hsu
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  2. Chih-Chieh Hung
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Corresponding author

Correspondence to Chih-Chieh Hung .

Editor information

Editors and Affiliations

  1. Deakin University, Geelong, Victoria, Australia

    Dinh Phung

  2. National Chiao Tung University, Hsinchu City, Taiwan

    Vincent S. Tseng

  3. Monash University, Clayton, Victoria, Australia

    Geoffrey I. Webb

  4. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Bao Ho

  5. University of Melbourne, Melbourne, Victoria, Australia

    Mohadeseh Ganji

  6. University of Melbourne, Melbourne, Victoria, Australia

    Lida Rashidi

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Hsu, FC., Hung, CC. (2018). Fusion of Modern and Tradition: A Multi-stage-Based Deep Network Approach for Head Detection. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10937. Springer, Cham. https://doi.org/10.1007/978-3-319-93034-3_32

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  • DOI: https://doi.org/10.1007/978-3-319-93034-3_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93033-6

  • Online ISBN: 978-3-319-93034-3

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