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.

    Creators(s): 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;
    Item type:Conference or Workshop Item(Poster)
    ID code:66617
    Keywords:micro-doppler, krawtchouk moments, Raspberry Pi, autonomous systems, Electrical engineering. Electronics Nuclear engineering, Electrical and Electronic Engineering
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:17 Jan 2019 12:07
    Last modified:05 Aug 2020 00:46
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/66617
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