Polygonal Shape-based Features for Pose Recognition using Kernel-SVM

Authors

  • M.F. Abu Hassan Department of Electrical, Electronic & Systems Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, Selangor, Malaysia. Universiti Kuala Lumpur, Malaysia France Institute, Bandar Baru Bangi, Selangor, Malaysia.
  • A. Hussain Department of Electrical, Electronic & Systems Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, Selangor, Malaysia
  • M.H.M. Saad Department of Mechanical & Material Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, Selangor, Malaysia.

Keywords:

Feature Extraction, Kernel-Support Vector Machine, Polygonal Shape, Pose Recognition,

Abstract

Pose recognition is an intriguing and challenging problem particularly in surveillance, inspection, etc. that lies in computer vision. This paper presents an efficient human walking and abnormal poses recognition system based on kernel-support vector machine (KSVM) using a novel feature set based on polygonal shape generalization on the human silhouette. The Shapiro-Wilk test was conducted to assess the data distribution and it summarized that the test rejected the hypothesis of normality for all features. Therefore, an inferential Mann-Whitney U test was performed to evaluate the proposed feature set statistically and results showed that all features were significantly different between the groups of poses (p < 0.001). Three kernel models: linear, polynomial and radial based function were adopted for SVM to classify the walking and abnormal poses. Results obtained showed that all three kernels of the KSVM classifiers performed well with accuracies of more than 95%. However, further experiments proved that the polynomial KSVM yields the best accuracy of 99.96%. Thus, it can be concluded that the proposed polygonal shape-based feature set is best paired with the polynomial KSVM for abnormal pose detection task.

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Published

2018-07-04

How to Cite

Abu Hassan, M., Hussain, A., & Saad, M. (2018). Polygonal Shape-based Features for Pose Recognition using Kernel-SVM. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-6), 41–46. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4367