Efficient micro-doppler based pedestrian activity classification for ADAS systems using Krawtchouk moments

Aßmann, A. and Izzo, A. and Clemente, Carmine (2016) Efficient micro-doppler based pedestrian activity classification for ADAS systems using Krawtchouk moments. In: 11th IMA International Conference on Mathematics in Signal Processing, 2016-12-12 - 2016-12-14, IET Austin Court.

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    Abstract

    In this paper the application, performance and results of a fully discrete micro-Doppler feature classication processing chain utilising Krawtchouk moment invariants are presented. The approach demonstrates to be capable of running on low power hardware such as the Raspberry Pi 2. The effectiveness of the proposed approach is veried through the use of real K-band data in real-time.

    ORCID iDs

    Aßmann, A., Izzo, A. ORCID logoORCID: https://orcid.org/0000-0001-6009-8757 and Clemente, Carmine ORCID logoORCID: https://orcid.org/0000-0002-6665-693X;
    CORE (COnnecting REpositories)