A New Bayesian Modeling for 3D Human-Object Action Recognition

Intelligent surveillance systems in human-centered environments require people behavioral monitoring. In this paper, we propose a new Bayesian framework to recognize actions on RGB-D videos by two different observations: the human pose and objects in its vicinity. We design a model for each action t...

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Bibliographic Details
Main Authors: Maurice, Camille, Madrigal, Francisco, Monin, Andre, Lerasle, Frederic
Format: Conference Proceeding
Language:English
Subjects:
Bayes methods
Mathematical model
Skeleton
Solid modeling
Three-dimensional displays
Two dimensional displays
Videos
Online Access:Request full text
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Summary:Intelligent surveillance systems in human-centered environments require people behavioral monitoring. In this paper, we propose a new Bayesian framework to recognize actions on RGB-D videos by two different observations: the human pose and objects in its vicinity. We design a model for each action that integrates these observations and a probabilistic sequencing of actions performed during activities. We validate our approach on two public video datasets: CAD-120 and Watch-n-Patch. We show a performance gain of 4% in action detection on the fly on CAD-120 videos. Our approach is competitive to 2D image features and skeleton-based methods, as we present an improvement of 16% on Watch-n-Patch. Action recognition performance is clearly improved by our Bayesian and joint human-object perception.
ISSN:2643-6213
DOI:10.1109/AVSS.2019.8909873