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: https://orcid.org/0000-0001-6009-8757 and Clemente, Carmine ORCID: https://orcid.org/0000-0002-6665-693X;
Item type: Conference or Workshop Item(Poster) ID code: 66617 Dates: DateEvent14 December 2016Published3 October 2016AcceptedKeywords: 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 May 2021 00:14 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/66617
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)