MutualCascade Method for Pedestrian Detection

  • Yanwen Chong
  • Qingquan Li
  • Hulin Kuang
  • Chun-Hou Zheng
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 304)


An effective and efficient feature selection method based on Gentle Adaboost (GAB) cascade and the Four Direction Feature (FDF), namely, MutualCascade, is proposed in this paper, which can be applied to the pedestrian detection problem in a single image. MutualCascade improves the classic method of cascade to remove irrelevant and redundant features. The mutual correlation coefficient is utilized as a criterion to determine whether a feature should be chosen or not. Experimental results show that the MutualCascade method is more efficient and effective than Voila and Jones’ cascade and some other Adaboost-based method, and is comparable with HOG-based methods. It also demonstrates a higher performance compared with the state-of-the-art methods.


FDF GAB MutualCascade Pedestrian Detection Feature Selection 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yanwen Chong
    • 1
    • 2
  • Qingquan Li
    • 1
    • 2
  • Hulin Kuang
    • 1
    • 2
    • 3
  • Chun-Hou Zheng
    • 4
  1. 1.State Key Laboratory for Information Engineering in Surveying, Mapping and Remote SensingWuhan UniversityWuhanChina
  2. 2.Engineering Research Center for Spatio-Temporal Data Smart Acquisition and ApplicationMinistry of Education of ChinaWuhanChina
  3. 3.School of electronic informationWuHan UniversityWuhanChina
  4. 4.College of Electrical Engineering and AutomationAnhui UniversityHefeiChina

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