Micro‐Doppler classification of human movements using spectrogram spatial features and support vector machine

Accurate distinction of dynamic moving objects especially in the context of security surveillance attracts great attention of researchers and practitioners. In the same context, present study proposes an advancement in feature extraction method from the micro‐Doppler spectrogram with the application of spatial statistics for moving human subject classification which minimizes the spectrogram analysis. A novel approach of spatial feature extraction from whole image spectrogram, followed by support vector machine (SVM) classifiers algorithm for multiclass classification, has been proposed in the present study. The proposed method has been tested for prediction accuracy and validated by applying on a very close and important five distinct human activities (which usually arise at any security observation site) as reported in the available literature. The results obtained adopting the proposed approach exhibit high accuracy for multiclass classification; yielding cross‐validation accuracy of 96.7% while actual predication of testing data provides the accuracy of 93.33%. For the prediction of accurate data classes, the post‐processing of the spectrogram prior to feature definition has also been performed using spatial based methods to enhance micro‐Doppler signatures.