Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition

TL;DR

This paper introduces a transfer learning framework that aligns infrared and visible light data in a common feature space to improve infrared human action recognition, leveraging auxiliary visible light data for enhanced performance.

Contribution

The paper proposes a novel Cross-Dataset Feature Alignment and Generalization (CDFAG) framework utilizing Kernel Manifold Alignment and dual encoders for cross-modal feature mapping.

Findings

Achieves state-of-the-art accuracy on InfAR dataset.

Effectively leverages visible light data to improve infrared recognition.

Demonstrates robustness across different datasets.

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 alignedto-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.