Abstract
Automotive radars are able to guarantee high performances at the expenses of a relatively low cost, and recently their application has been extended to several fields in addition to the original one. In this paper we consider the use of this kind of radars to discriminate different types of people’s movements in a real context. To this end, we exploit two different maps obtained from radar, that is, a spectrogram and a range-Doppler map. Through the application of dimensionality reduction methods, such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) algorithm, and the use of machine learning techniques we prove that is possible to classify with a very good precision people’s way of walking even employing commercial devices specifically designed for other purposes.
Highlights
Recognition of a person’s type of movement has implications for many aspects of daily life, from security applications to monitoring for assisted living
Speed and hands movement classification is performed by using Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE)
It is possible to observe that the number of principal components (or number of dimension in case of t-distributed Stochastic Neighbor Embedding (t-SNE) ) that here corresponds to the number of features, has a small impact on the classification performance
Summary
Recognition of a person’s type of movement has implications for many aspects of daily life, from security applications to monitoring for assisted living. Discriminating whether a person is running or walking normally in airports or shopping centers, for example, may help video surveillance to detect possible dangerous situations [1,2,3]. Tools designed for this purpose involve the use of contactless devices, and radar technology is suitable for the mentioned scenario. In this paper we consider the use of an automotive radar to classify different types of monitored actions. Speed and hands movement classification is performed by using Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE)
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