Human Activity Detection and Coarse Localization Outdoors Using Micro-Doppler Signatures

Human activity detection outdoors is emerging as a very important research field due to its potential application in surveillance, assisted living, search and rescue, and military applications. For such applications it is important to have detailed information about the human target, for example, whether the detected target is a single person or a group of people, what activity a target is performing, and the rough location of the target. In this paper, we propose novel usage of machine learning techniques to perform subject classification, human activity classification, people counting, and coarse localization by classifying micro-Doppler signatures obtained from a low-cost and low-power radar system. Our experiments were performed outdoors. For feature extraction of micro-Doppler signatures, we applied a two-directional two-dimensional principle component analysis (2D2PCA). Our results show that by applying 2D2PCA, the accuracy results of support vector machine (SVM) and
${k}$
-nearest neighbors (
${k}$
NN) classifiers were greatly improved. We also designed and implemented a Convolutional Neural Network (CNN) for the target classifications in terms of type, number, activity, and coarse localization. Our CNN model obtained very high classification accuracies (97% to 100%), which are superior to the best results obtained by SVM and
${k}$
NN. Finally, we investigated the effects of the frame length of the sliding window, the angle of the direction of movement, and the number of radars used on the classification performance, providing valuable guidelines for machine learning modeling and experimental setup of micro-Doppler based research and applications.