Sample weights determination based on cosine similarity method as an extension to infrared action recognition

Abstract

The widespread application of infrared human action recognition in intelligent surveillance has attracted significant attention. However, the infrared action recognition dataset is limited, which limits the development of infrared action recognition. Existing methods for infrared action recognition are based on features in the same sample, without paying attention to within-class differences. Motivated by the idea of weighting video information, this paper proposes a novel infrared action recognition framework to reweight the samples of training sets named REWS to solve the problems of limited infrared action data and the large within-class differences in the infrared action recognition dataset. In the proposed framework, we first map infrared action video data to a low-dimensional feature space, and use the cosine similarity between the feature data of the training set and the testing set to determine the weight of the training set samples. Each training set sample has an independent weight. Then, a support vector machine (SVM) is trained by the training sets with weights to recognize the infrared actions. Experimental results demonstrate that our approach can achieve state-of-the-art performance compared with hand-crafted features based methods on the benchmark InfAR dataset.