A New Hybrid Architecture for Human Activity Recognition from RGB-D videos

Activity Recognition from RGB-D videos is still an open problem due to the presence of large varieties of actions. In this work, we propose a new architecture by mixing a high level handcrafted strategy and machine learning techniques. We propose a novel two level fusion strategy to combine features from different cues to address the problem of large variety of actions. As similar actions are common in daily living activities, we also propose a mechanism for similar action discrimination. We validate our approach on four public datasets, CAD-60, CAD-120, MSRDailyActivity3D, and NTU-RGB+D improving the state-of-the-art results on them.