Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition. (15th February 2018)

• Record Type: Journal Article
• Authors: Liu, Yang; Lu, Zhaoyang; Li, Jing; Yao, Chao; Deng, Yanzi
• Other Names: Pan Shirui Academic Editor.
• Is Part Of: Complexity. Volume 2018(2018)
• Publication Date: 2018-02-15
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2018/5345241
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : 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.