Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition
Recently, infrared human action recognition has attracted increasing attention for it has many advantages over visible light, that is, being robust to illumination change and shadows. However, the infrared action data is limited until now, which degrades the performance of infrared action recognitio...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/5345241 |
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| author | Yang Liu Zhaoyang Lu Jing Li Chao Yao Yanzi Deng |
| author_facet | Yang Liu Zhaoyang Lu Jing Li Chao Yao Yanzi Deng |
| author_sort | Yang Liu |
| collection | DOAJ |
| description | Recently, infrared human action recognition has attracted increasing attention for it has many advantages over visible light, that is, being robust to illumination change and shadows. However, the infrared action data is limited until now, which degrades the performance of infrared action recognition. Motivated by the idea of transfer learning, an infrared human action recognition framework using auxiliary data from visible light is proposed to solve the problem of limited infrared action data. In the proposed framework, we first construct a novel Cross-Dataset Feature Alignment and Generalization (CDFAG) framework to map the infrared data and visible light data into a common feature space, where Kernel Manifold Alignment (KEMA) and a dual aligned-to-generalized encoders (AGE) model are employed to represent the feature. Then, a support vector machine (SVM) is trained, using both the infrared data and visible light data, and can classify the features derived from infrared data. The proposed method is evaluated on InfAR, which is a publicly available infrared human action dataset. To build up auxiliary data, we set up a novel visible light action dataset XD145. Experimental results show that the proposed method can achieve state-of-the-art performance compared with several transfer learning and domain adaptation methods. |
| format | Article |
| id | doaj-art-eccdc2b9b6bf4e02933270435e3d3d96 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-eccdc2b9b6bf4e02933270435e3d3d962025-08-20T02:21:47ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/53452415345241Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action RecognitionYang Liu0Zhaoyang Lu1Jing Li2Chao Yao3Yanzi Deng4School of Telecommunications Engineering, Xidian University, No. 2 South Taibai Road, Xi’an, Shaanxi 710071, ChinaSchool of Telecommunications Engineering, Xidian University, No. 2 South Taibai Road, Xi’an, Shaanxi 710071, ChinaSchool of Telecommunications Engineering, Xidian University, No. 2 South Taibai Road, Xi’an, Shaanxi 710071, ChinaSchool of Automation, Northwestern Polytechnical University, No. 127, West Youyi Road, Xi’an, Shaanxi 710072, ChinaSchool of Telecommunications Engineering, Xidian University, No. 2 South Taibai Road, Xi’an, Shaanxi 710071, ChinaRecently, infrared human action recognition has attracted increasing attention for it has many advantages over visible light, that is, being robust to illumination change and shadows. However, the infrared action data is limited until now, which degrades the performance of infrared action recognition. Motivated by the idea of transfer learning, an infrared human action recognition framework using auxiliary data from visible light is proposed to solve the problem of limited infrared action data. In the proposed framework, we first construct a novel Cross-Dataset Feature Alignment and Generalization (CDFAG) framework to map the infrared data and visible light data into a common feature space, where Kernel Manifold Alignment (KEMA) and a dual aligned-to-generalized encoders (AGE) model are employed to represent the feature. Then, a support vector machine (SVM) is trained, using both the infrared data and visible light data, and can classify the features derived from infrared data. The proposed method is evaluated on InfAR, which is a publicly available infrared human action dataset. To build up auxiliary data, we set up a novel visible light action dataset XD145. Experimental results show that the proposed method can achieve state-of-the-art performance compared with several transfer learning and domain adaptation methods.http://dx.doi.org/10.1155/2018/5345241 |
| spellingShingle | Yang Liu Zhaoyang Lu Jing Li Chao Yao Yanzi Deng Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition Complexity |
| title | Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition |
| title_full | Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition |
| title_fullStr | Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition |
| title_full_unstemmed | Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition |
| title_short | Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition |
| title_sort | transferable feature representation for visible to infrared cross dataset human action recognition |
| url | http://dx.doi.org/10.1155/2018/5345241 |
| work_keys_str_mv | AT yangliu transferablefeaturerepresentationforvisibletoinfraredcrossdatasethumanactionrecognition AT zhaoyanglu transferablefeaturerepresentationforvisibletoinfraredcrossdatasethumanactionrecognition AT jingli transferablefeaturerepresentationforvisibletoinfraredcrossdatasethumanactionrecognition AT chaoyao transferablefeaturerepresentationforvisibletoinfraredcrossdatasethumanactionrecognition AT yanzideng transferablefeaturerepresentationforvisibletoinfraredcrossdatasethumanactionrecognition |