Authors:
Naoto Ienaga
;
Yuko Ozasa
and
Hideo Saito
Affiliation:
Keio University, Japan
Keyword(s):
Action Recognition, Long Short-term Memory, Information Fusion.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Image Understanding
;
Learning of Action Patterns
;
Pattern Recognition
;
Software Engineering
;
Video Analysis
Abstract:
Recent years have seen the introduction of service robots as waiters or waitresses in restaurants and cafes.
In such venues, it is common for customers to visit in groups as well as for them to engage in conversation
while eating and drinking. It is important for cyber serving staff to understand whether they are eating and
drinking, or not, in order to wait on tables at appropriate times. In this paper, we present a method by which
the robots can recognize eating and drinking actions performed by individuals in a group. Our approach uses
the positions of joints in the human body as a feature and long short-term memory to achieve a recognition
task on time-series data. We also used head directions in our method, as we assumed that it is effective for
recognition in a group. The information garnered from head directions and joint positions is integrated via
logistic regression and employed in recognition. The results show that this yielded the highest accuracy and
effectiveness of the
robots’ tasks.
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