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. ![]() ![]() | Item type: | Conference or Workshop Item(Poster) |
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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: | 02 Feb 2021 09:09 |
Related URLs: | |
URI: | https://strathprints.strath.ac.uk/id/eprint/66617 |
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