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

Research output: Contribution to conferencePoster

15 Downloads (Pure)

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.
Original languageEnglish
Number of pages6
Publication statusPublished - 14 Dec 2016
Event11th IMA International Conference on Mathematics in Signal Processing - IET Austin Court, Birmingham, United Kingdom
Duration: 12 Dec 201614 Dec 2016
http://www.ima.org.uk/conferences/conferences_calendar/11th_maths_in_signal_processing.html

Conference

Conference11th IMA International Conference on Mathematics in Signal Processing
CountryUnited Kingdom
CityBirmingham
Period12/12/1614/12/16
Internet address

    Fingerprint

Keywords

  • micro-doppler
  • krawtchouk moments
  • Raspberry Pi
  • autonomous systems

Cite this

Aßmann, A., Izzo, A., & Clemente, C. (2016). Efficient micro-doppler based pedestrian activity classification for ADAS systems using Krawtchouk moments. Poster session presented at 11th IMA International Conference on Mathematics in Signal Processing , Birmingham, United Kingdom.