DocumentCode :
602443
Title :
Human-object-object-interaction affordance
Author :
Shaogang Ren ; Yu Sun
Author_Institution :
Univ. of South Florida, Tampa, FL, USA
fYear :
2013
fDate :
15-17 Jan. 2013
Firstpage :
1
Lastpage :
6
Abstract :
This paper presents a novel human-object-object (HOO) interaction affordance learning approach that models the interaction motions between paired objects in a human-object-object way and use the motion models to improve the object recognition reliability. The innate interaction-affordance knowledge of the paired objects is modeled from a set of labeled training data that contains relative motions of the paired objects, humans actions, and object labels. The learned knowledge of the pair relationship is represented with a Bayesian Network and the trained network is used to improve recognition reliability of the objects.
Keywords :
belief networks; image motion analysis; learning (artificial intelligence); object recognition; Bayesian network; HOO; human-object-object-interaction affordance learning approach; humans actions; motion models; object labels; object recognition reliability; paired objects; trained network; Bayes methods; Educational institutions; Motion segmentation; Tracking; Training; Training data; Trajectory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robot Vision (WORV), 2013 IEEE Workshop on
Conference_Location :
Clearwater Beach, FL
Print_ISBN :
978-1-4673-5646-6
Electronic_ISBN :
978-1-4673-5647-3
Type :
conf
DOI :
10.1109/WORV.2013.6521912
Filename :
6521912
Link To Document :
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