SAKHALKAR , Kaustubh

This paper address the recognition of short-term daily living actions from RGB-D videos. The existing approaches ignore spatio-temporal contextual relationships in the action videos. So, we propose to explore the spatial layout to better model the appearance. In order to encode temporal information, we divide the action sequence into temporal...

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...