IEIE Transactions on Smart Processing and Computing

The Institute of Electronics and Information Engineers (대한전자공학회)
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Classification Algorithms for Human and Dog Movement Based on Micro-Doppler Signals

  • Lee, Jeehyun (Department of Electronic Engineering, Sogang University) ;
  • Kwon, Jihoon (Graduate School of Convergence Science and Technology, Seoul National University) ;
  • Bae, Jin-Ho (Department of Ocean System Engineering, Jeju National University) ;
  • Lee, Chong Hyun (Department of Ocean System Engineering, Jeju National University)
  • Received : 2017.01.04
  • Accepted : 2017.01.31
  • Published : 2017.02.28
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Abstract

We propose classification algorithms for human and dog movement. The proposed algorithms use micro-Doppler signals obtained from humans and dogs moving in four different directions. A two-stage classifier based on a support vector machine (SVM) is proposed, which uses a radial-based function (RBF) kernel and $16^{th}$-order linear predictive code (LPC) coefficients as feature vectors. With the proposed algorithms, we obtain the best classification results when a first-level SVM classifies the type of movement, and then, a second-level SVM classifies the moving object. We obtain the correct classification probability 95.54% of the time, on average. Next, to deal with the difficult classification problem of human and dog running, we propose a two-layer convolutional neural network (CNN). The proposed CNN is composed of six ($6{\times}6$) convolution filters at the first and second layers, with ($5{\times}5$) max pooling for the first layer and ($2{\times}2$) max pooling for the second layer. The proposed CNN-based classifier adopts an auto regressive spectrogram as the feature image obtained from the $16^{th}$-order LPC vectors for a specific time duration. The proposed CNN exhibits 100% classification accuracy and outperforms the SVM-based classifier. These results show that the proposed classifiers can be used for human and dog classification systems and also for classification problems using data obtained from an ultra-wideband (UWB) sensor.

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References

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