{"title":"Human Action Recognition with Skeleton and Infrared Fusion Model","authors":"Amine Mansouri, Toufik Bakir, S. Femmam","doi":"10.18178/joig.11.4.309-320","DOIUrl":null,"url":null,"abstract":"Skeleton-based human action recognition conveys interesting information about the dynamics of a human body. In this work, we develop a method that uses a multi-stream model with connections between the parallel streams. This work is inspired by a state-of-the-art method called FUSIONCPA that merges different modalities: infrared input and skeleton input. Because we are interested in investigating improvements related to the skeleton-branch backbone, we used the Spatial-Temporal Graph Convolutional Networks (ST-GCN) model and an EfficientGCN attention module. We aim to provide improvements when capturing spatial and temporal features. In addition, we exploited a Graph Convolutional Network (GCN) implemented in the ST-GCN model to capture the graphic connectivity in skeletons. This paper reports interesting accuracy on a large-scale dataset (NTU-RGB+D 60), over 91% and 93% on respectively crosssubject, and cross-view benchmarks. This proposed model is lighter by 9 million training parameters compared with the model FUSION-CPA.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国图象图形学报","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.18178/joig.11.4.309-320","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0
Abstract
Skeleton-based human action recognition conveys interesting information about the dynamics of a human body. In this work, we develop a method that uses a multi-stream model with connections between the parallel streams. This work is inspired by a state-of-the-art method called FUSIONCPA that merges different modalities: infrared input and skeleton input. Because we are interested in investigating improvements related to the skeleton-branch backbone, we used the Spatial-Temporal Graph Convolutional Networks (ST-GCN) model and an EfficientGCN attention module. We aim to provide improvements when capturing spatial and temporal features. In addition, we exploited a Graph Convolutional Network (GCN) implemented in the ST-GCN model to capture the graphic connectivity in skeletons. This paper reports interesting accuracy on a large-scale dataset (NTU-RGB+D 60), over 91% and 93% on respectively crosssubject, and cross-view benchmarks. This proposed model is lighter by 9 million training parameters compared with the model FUSION-CPA.