NDLI: Recognition of two-person interactions using a hierarchical Bayesian network

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Content Provider	ACM Digital Library
Author	Park, Sangho Aggarwal, J. K.
Abstract	Recognizing human interactions is a challenging task due to the multiple body parts of interacting persons and the concomitant occlusions. This paper presents a method for the recognition of two-person interactions using a hierarchical Bayesian network (BN). The poses of simultaneously tracked body parts are estimated at the low level of the BN, and the overall body pose is estimated at the high level of the BN. The evolution of the poses of the multiple body parts are processed by a dynamic Bayesian network. The recognition of two-person interactions are expressed in terms of semantic verbal descriptions at multiple levels; individual body-part motions at low level, single-person actions at middle level, and twoperson interactions at high level. Example sequences of interacting persons illustrate the success of the proposed framework.
Starting Page	65
Ending Page	76
Page Count	12
File Format	PDF
ISBN	158113780X
DOI	10.1145/982452.982461
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2003-11-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Hierarchy Motion Scene understanding Event recognition Surveillance Bayesian network Human interaction
Content Type	Text
Resource Type	Article

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Recognition of two-person interactions using a hierarchical Bayesian network