Abstract
The spectrotemporal representation of an ultrasonar wave reflected by an object contains frequency shifts corresponding to the velocity of the object’s moving parts, also known as the micro-Doppler signature. The present study describes how the micro-Doppler signature of human subjects, collected in two experiments, can be used to categorize the action performed by the subject. The proposed method segments the spectrogram into temporal events, learns prototypes and categorizes the events using a Nearest Neighbour approach. Results show an average accuracy above 95%, with some categories reaching 100%, and a strong robustness to variations in the model parameters. The low computational cost of the system, together with its high accuracy, even for short length inputs, make it appropriate for a real-time implementation with applications to intelligent surveillance, monitoring and related disciplines.
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Dura-Bernal, S. et al. (2011). Human Action Categorization Using Ultrasound Micro-Doppler Signatures. In: Salah, A.A., Lepri, B. (eds) Human Behavior Understanding. HBU 2011. Lecture Notes in Computer Science, vol 7065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25446-8_3
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DOI: https://doi.org/10.1007/978-3-642-25446-8_3
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