Classification of Indian Classical Dance Steps using HOG Features

نویسندگان

  • Surbhi Gautam
  • Garima Joshi
  • Nidhi Garg
چکیده

In this paper Histogram Oriented Gradient (HOG) features are extracted to classify the postures in a Indian classical dance video dataset. The aim is to design an automated system that can recognize the steps of Indian classical dance in a video. As a video consists of frames of different actions, so features representing shapes can be used to interpret the dance steps. HOG based features are capable of representing the shape in varying background conditions. The proposed system performance is analyzed for total number of 50 poses taken from 9 different Bharatanatyam videos in varying background conditions. To find an optical size of HOG based features Taguchi analysis for L-9 orthogonal array is implemented. KeywordsHistogram Oriented Gradient, Human activity recognitionIndian Classical Dance dataset, SVM

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تاریخ انتشار 2017