Homogeneity Property Based Two Wheeler Detection Research Using Histogram of Oriented Gradients and Soft Computing

  • Authors

    • Yeunghak Lee
    • Israfil Ansari
    • Jaechang Shim
    https://doi.org/10.14419/ijet.v7i4.36.29010
  • Object detection, Histogram of oriented gradients, Homogeneity, Adaboost, Two-wheelers.

  • Abstract

    We describe an algorithm to detect two-wheelers with various shapes and viewpoints using the homogeneity property and Histograms of Gradients (HOG). The typical shape of a two-wheeler is can be divided into two parts: human body (upper area) and assembled complex components (bottom). The upper area is substantially homogeneous, because the front and rear body views are simple, whereas the bottom part consists of complex shapes and shows little homogeneity. And the bottom area has very little homogeneity, because it is consisted of complicated shapes. Our algorithm using HOG features based on homogeneity to the upper and lower parts separately and uses Adaboost as the classifier. Furthermore, this paper applied the Adaboost algorithm to classify the objects as the soft computing. Our improved algorithm correctly identified more than 84% of the hardest case tested – motorcycles leaning at 60º - and more than 98% of all types of two-wheelers at 90º.

     

     

  • References

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  • How to Cite

    Lee, Y., Ansari, I., & Shim, J. (2018). Homogeneity Property Based Two Wheeler Detection Research Using Histogram of Oriented Gradients and Soft Computing. International Journal of Engineering & Technology, 7(4.36), 1472-1475. https://doi.org/10.14419/ijet.v7i4.36.29010

    Received date: 2019-04-26

    Accepted date: 2019-04-26