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SSM-Based Joint Dictionary Learning for Cross-View Action Recognition

Human action recognition is a central problem in many visual intelligence applications, such as video surveillance, clinical care and ambient-assisted living. However, the same action is variable under different viewpoints, and the self-similarity matrix (SSM) methods get worse when the viewpoint changes greatly. In this paper, we propose a novel approach, referred as SSM-based joint dictionary learning (SJDL), which can obtain more discriminative features for cross-view action recognition. The approach learns a set of view-specific dictionaries for each view to align the view-specific sparse representations and a common dictionary shared by all views to obtain view-shared sparse representations. Those dictionaries are learned by enforcing sparse representations of different views to have concise and similar patterns. The superior performance of SJDL is demonstrated by experimental results on the IXMAS dataset.