Paper Info
Title
Improved Semantic-Based Human Interaction Understanding Using Context-Based Knowledge
Abstract
This paper proposes a descriptive approach for context-based human activity analysis through an hierarchical framework in a scene understanding application. Each human movement with respect to himself, others and scene, can arise different layers of human activities analysis, which usually investigated separately depend on the application. Human behaviour can not be analysed properly, since the all different layers of information were not considered. The effect of using the different layers of information to increase the accuracy of the analysis is presented in the study. The contributions are, using different information layers such as human body parts movement and human-object interaction, in 3D space, to improve human activity analysis, and proposing a probabilistic and descriptive model, based on a well-known human movement descriptor and Bayesian Network (BN) approach. Thus, based on the mentioned framework, the model is generalizable and flexible which are necessary for having such an applicable system. The capability of the proposed approach is presented in the experiment's section.
Year
DOI
Venue
2013
10.1109/SMC.2013.494
SMC
Keywords
Field
DocType
feature extraction,pose estimation,probability
Computer science,Context based,Human interaction,Feature extraction,Pose,Bayesian network,Artificial intelligence,Probabilistic logic,Machine learning,Human body,Bayesian probability
Conference
ISSN
Citations 
PageRank 
1062-922X
3
0.38
References 
Authors
9
2
Name
Order
Citations
PageRank
Kamrad Khoshhal Roudposhti1272.82
Jorge Dias255651.00