Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos
This work addresses action recognition for video analysis applications, representing an incremental improvement through novel multimodal feature integration.
The paper tackled action recognition in RGB+D videos by proposing a deep autoencoder-based feature factorization network and structured sparsity learning machine, achieving state-of-the-art accuracy on five benchmark datasets.
Single modality action recognition on RGB or depth sequences has been extensively explored recently. It is generally accepted that each of these two modalities has different strengths and limitations for the task of action recognition. Therefore, analysis of the RGB+D videos can help us to better study the complementary properties of these two types of modalities and achieve higher levels of performance. In this paper, we propose a new deep autoencoder based shared-specific feature factorization network to separate input multimodal signals into a hierarchy of components. Further, based on the structure of the features, a structured sparsity learning machine is proposed which utilizes mixed norms to apply regularization within components and group selection between them for better classification performance. Our experimental results show the effectiveness of our cross-modality feature analysis framework by achieving state-of-the-art accuracy for action classification on five challenging benchmark datasets.