关键词: 红外, 人体目标识别, 多特征, 降维, 迁移学习

Abstract: Aiming at the poor recognition accuracy and robustness of the human targets caused by the serious interference on the targets under infrared imaging conditions, an infrared human target recognition method based on multi-feature dimensionality reduction and transfer learning was proposed. Firstly, in order to solve the problem of incomplete information during the extraction of a single feature by the traditional infrared human target feature extraction method, different kinds of heterogeneous features were extracted to fully exploit the characteristics of infrared human targets. Secondly, to provide the efficient and compact feature description for subsequent recognition, a principal component analysis method was utilized to reduce the dimensionality of the fused heterogeneous features. Finally, to solve the problems such as poor generalization performance, caused by the lack of tagged human target samples in infrared images as well as the distributional and semantic deviations between the training samples and testing samples, an effective infrared human target classifier based on transfer learning was presented, which was able to greatly improve the generalization performance and the target recognition accuracy. The experimental results show that the recognition accuracy of the method on infrared human target data set reaches more than 94%, which is better and more stable than that of the methods with a single feature such as Histogram of Oriented Gradients (HOG), Intensity Self Similarity (ISS) for feature representation or the methods learned with traditional non-transfer classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN). Therefore, the performance of infrared human target recognition is improved in real complex scenes by the method.

Key words: infrared, human target recognition, multi-feature, dimensionality reduction, transfer learning