Deep learning-based human posture recognition

Adam Ayre-Storie, Li Zhang

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

Abstract

Human posture recognition using real-life images is a challenging task. In order to devise effective spatial-temporal representations, we propose a hybrid model by integrating object detection, segmentation and classification methods for the recognition of three human postures, i.e. jumping, sitting and standing, from challenging real-world images. Specifically, a well-known deep learning model, i.e. Mask R-CNN, is first employed to detect and segment each human subject in an image. The extracted regional features from each segmented region are then passed to a revised Inception-ResNet-v2 model for posture recognition. In particular, the revised Inception-ResNet-v2 model shows great efficiency in deep feature extraction. Evaluated using human action data sets, the proposed hybrid model outperforms two other deep learning methods for posture classification. This two-stage process provides a fundamental step for future research, such as human recognition within a commercial environment.

Original languageEnglish
Title of host publicationProceedings of 2021 International Conference on Machine Learning and Cybernetics, ICMLC 2021
PublisherIEEE
ISBN (Electronic)9781665466080
DOIs
Publication statusPublished - 2021
Event20th International Conference on Machine Learning and Cybernetics, ICMLC 2021 - Adelaide, United States
Duration: 4 Dec 20215 Dec 2021

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume2021-December
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference20th International Conference on Machine Learning and Cybernetics, ICMLC 2021
Country/TerritoryUnited States
CityAdelaide
Period4/12/215/12/21

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