No abstract

Human motion detection, classification, and perceiving, the dynamics of moving objects in the environment, are crucial in many applications. Many sensors have been used to this detection; the radar represents one of the promising sensors. Kalman filter (KF) and convolutional neural network (CNN) represents a powerful tool for estimation and classification respectively. In this paper, a combination between the KF and CNN have been proposed to detect and classify human behavior. This proposal presents two important points, the precise features map from the combination of Kalman Filter and CNN, as well as the use of the radar, which is working under all circumstances and does not break the privacy. Twenty different experiments with three scenarios for different motion with and without glass wall have been studied, and they are classified. The results show that the overperform of the proposed algorithm and the classification accuracy can reach 98.7%. This advancement of the proposed algorithm depends on the efficient Wigner-Ville short time Fourier transform (STFT) which is used as a feature extractor and make Range-Doppler (RD) map.

No abstract

No abstract

No abstract