Title

Human-Object-Object-Interaction Affordance

Document Type

Conference Proceeding

Publication Date

1-2013

Keywords

object recognition, belief networks, image motion analysis, learning (artificial intelligence), trained network, human-object-object-interaction affordance learning approach, HOO, motion models, object recognition reliability, paired objects, humans actions, object labels, Bayesian network

Digital Object Identifier (DOI)

https://doi.org/10.1109/WORV.2013.6521912

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.

Was this content written or created while at USF?

Yes

Citation / Publisher Attribution

2013 IEEE Workshop on Robot Vision (WORV), Clearwater Beach, FL, 2013, p. 1-6.