An Analysis of Concealed Object Detection Using Decision Tree and Random Forest Algorithms

  • Authors

    • Pallavi Shintre
    • Shweatlana Mahapatra
    • Shweta Vincent
    • Om Prakash Kumar
    2018-12-19
    https://doi.org/10.14419/ijet.v7i4.41.24518
  • Decision tree algorithm, Random forest algorithm, Haar wavelet transform, Millimeter waves .

  • Abstract

    This paper presents a comparative study of the Decision tree algorithm and Random Forest algorithm, both using Haar wavelet transform to classify a concealed object as a threat or not. This finds its applications in airports and railway stations where passenger security is a major concern. A sub-millimeter wave image of a person having a concealed weapon on his thigh has been treated as the test dataset. The Haar wavelet transform along with the aforementioned algorithms is applied on the image to classify the patches in the images as threat or no threat regions. It is found that the Random Forest algorithm outperforms the Decision tree algorithm in terms of accuracy of detection as well as number of false positive generation.

     

     

     

  • References

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  • How to Cite

    Shintre, P., Mahapatra, S., Vincent, S., & Prakash Kumar, O. (2018). An Analysis of Concealed Object Detection Using Decision Tree and Random Forest Algorithms. International Journal of Engineering & Technology, 7(4.41), 154-157. https://doi.org/10.14419/ijet.v7i4.41.24518

    Received date: 2018-12-21

    Accepted date: 2018-12-21

    Published date: 2018-12-19