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.

Preview

Text. Filename: Abmann_ICMSP_2016_Efficient_micro_doppler_based_pedestrian_activity_classification.pdf Accepted Author Manuscript Download (1MB)| Preview

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

Izzo, A. ORCID: https://orcid.org/0000-0001-6009-8757

Clemente, Carmine ORCID: https://orcid.org/0000-0002-6665-693X

• Share and Export
  

  RDF+XMLBibTeXRIOXX2 XMLRDF+N-TriplesJSONDublin CoreAtomSimple MetadataReferMETSHTML CitationASCII CitationOpenURL ContextObjectEndNoteOpenURL ContextObject in SpanMODSMPEG-21 DIDLEP3 XMLData Cite XMLRIS (EndNote Online, Zotero, Ref manager, etc)RDF+N3Multiline CSV
• Item metadata

  Item type:	Conference or Workshop Item(Poster)
  ID code:	66617
  Dates:	Date Event 14 December 2016 Published 3 October 2016 Accepted
  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:	11 Nov 2024 16:56
  Related URLs:	Event
  URI:	https://strathprints.strath.ac.uk/id/eprint/66617