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

Abstract : 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.
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Submitted on : Monday, October 15, 2018 - 5:42:54 PM
Last modification on : Thursday, August 4, 2022 - 5:19:01 PM
Long-term archiving on: : Wednesday, January 16, 2019 - 4:06:29 PM


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Srijan Das, Monique Thonnat, Kaustubh Sakhalkar, Michal F Koperski, Francois Bremond, et al.. A New Hybrid Architecture for Human Activity Recognition from RGB-D videos. MMM 2019 - 25th International Conference on MultiMedia Modeling, Jan 2019, Thessaloniki, Greece. pp.493-505, ⟨10.1007/978-3-030-05716-9_40⟩. ⟨hal-01896061⟩



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