GAITPOINT: A GAIT RECOGNITION NETWORK BASED ON POINT CLOUD ANALYSIS

Jiajing Chen, Huantao Ren, Frank Chen, Senem Velipasalar, Vir V. Phoha

Research output: Chapter in Book/Entry/PoemConference contribution

Abstract

We propose a novel gait recognition method that combines convolutional features with features of human pose key points obtained by a point cloud analysis model. Currently, most state-of-the-art works on gait recognition rely on only images and are purely based on convolutional neural networks. Most of these methods are very sensitive to small variations in the appearance of a walking person. For instance, if a person wears a coat or carries a bag, the accuracy of these methods may drop significantly. To address this problem, we propose to treat a sequence of human key points as a point cloud and combine human key point features and convolution feature map for final prediction. The experimental results show the promise of this approach, which outperforms three state-of-the-art baselines in all walking scenarios, including the ones involving heavy clothing or carried items.

Original languageEnglish (US)
Title of host publication2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
PublisherIEEE Computer Society
Pages1916-1920
Number of pages5
ISBN (Electronic)9781665496209
DOIs
StatePublished - 2022
Event29th IEEE International Conference on Image Processing, ICIP 2022 - Bordeaux, France
Duration: Oct 16 2022Oct 19 2022

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference29th IEEE International Conference on Image Processing, ICIP 2022
Country/TerritoryFrance
CityBordeaux
Period10/16/2210/19/22

Keywords

  • Convolution feature map
  • Gait recognition
  • Human key points
  • Point cloud

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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