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Egocentric Activity Recognition using Histogram Oriented Features and Textural Features

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  • K. P. Sanal Kumar
  • R. Bhavani

Abstract

Recognizing egocentric actions is a challenging task that has to be addressed in recent years. The recognition of first person activities helps in assisting elderly people, disabled patients and so on. Here, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. In this paper, the recognition is done using histogram oriented features like Histogram of Oriented Gradients (HOG), Histogram of optical Flow (HOF) and Motion Boundary Histogram (MBH) and textural features like Gray Level co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP). The features like Histogram of Oriented Gradients (HOG), Histogram of optical Flow (HOF) and Motion Boundary Histogram (MBH) are combined together to form a feature (Combined HHM). The extracted features are fed as input to Principal component Analysis (PCA) which reduces the feature dimensionality. The reduced features are given as input to the classifiers like Support Vector Machine (SVM), k Nearest Neighbor (kNN) and Probabilistic Neural Network (PNN). The performance results showed that SVM gave better results than kNN and PNN classifier for both categories.

Suggested Citation

  • K. P. Sanal Kumar & R. Bhavani, 2017. "Egocentric Activity Recognition using Histogram Oriented Features and Textural Features," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 2(6), pages 397-405, December.
    • Handle: RePEc:jbh:ijsrcs:v2:y2017:i6:id:hcseit1726120
    Note: Article URL: https://ijsrcseit.com/CSEIT1726120
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