Zero-Shot Motion Pattern Recognition from 4D Point-Clouds

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Abstract

We address a timely and relevant problem in signal processing: The recognition of patterns from spatial data in motion through a zero-shot learning scenario. We introduce a neural network architecture based on Siamese networks to recognize unseen classes of motion patterns. The approach uses a graph-based technique to achieve permutation invariance and also encodes moving point clouds into a representation space in a computationally efficient way. We evaluated the model on an open dataset with twenty-one gestures. The model out-performes state-of-the-art architectures with a considerable margin in four different settings in terms of accuracy while reducing the computational complexity up to 60 times.
Original languageEnglish
Title of host publication2021 IEEE 31st International Workshop on Machine Learning for Signal Processing, MLSP 2021
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)978-1-7281-6338-3
ISBN (Print)978-1-6654-1184-4
DOIs
Publication statusPublished - 15 Nov 2021
MoE publication typeA4 Article in a conference publication
EventIEEE International Workshop on Machine Learning for Signal Processing - Gold Coast, Australia
Duration: 25 Oct 202128 Oct 2021
Conference number: 31
https://2021.ieeemlsp.org/

Workshop

WorkshopIEEE International Workshop on Machine Learning for Signal Processing
Abbreviated titleMLSP
Country/TerritoryAustralia
CityGold Coast
Period25/10/202128/10/2021
Internet address

Keywords

  • Training
  • Computational modeling
  • Conferences
  • Neural networks
  • Computer architecture
  • Machine learning
  • Signal processing

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