LABEL-AWARE RANKED LOSS FOR ROBUST PEOPLE COUNTING USING AUTOMOTIVE IN-CABIN RADAR

Lorenzo Servadei, Huawei Sun, Julius Ott, Michael Stephan, Souvik Hazra, Thomas Stadelmayer, Daniela Sanchéz Lopera, Robert Wille, Avik Santra

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

In this paper, we introduce the Label-Aware Ranked loss, a novel metric loss function. Compared to the state-of-the-art Deep Metric Learning losses, this function takes advantage of the ranked ordering of the labels in regression problems. To this end, we first show that the loss minimises when datapoints of different labels are ranked and laid at uniform angles between each other in the embedding space. Then, to measure its performance, we apply the proposed loss on a regression task of people counting with a short-range radar in a challenging scenario, namely a vehicle cabin. The introduced approach improves the accuracy as well as the neighboring labels accuracy up to 83.0% and 99.9%: An increase of 6.7% and 2.1% on state-of-the-art methods, respectively.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3883-3887
Number of pages5
ISBN (Electronic)9781665405409
DOIs
StatePublished - 2022
Externally publishedYes
Event47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore
Duration: 23 May 202227 May 2022

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2022-May
ISSN (Print)1520-6149

Conference

Conference47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Country/TerritorySingapore
CityVirtual, Online
Period23/05/2227/05/22

Keywords

  • Deep Metric Learning
  • People Counting
  • Radar Signal Processing

Fingerprint

Dive into the research topics of 'LABEL-AWARE RANKED LOSS FOR ROBUST PEOPLE COUNTING USING AUTOMOTIVE IN-CABIN RADAR'. Together they form a unique fingerprint.

Cite this