[1]NIE Huirao,TAO Linmi.Inference framework for activity recognition based on multiple semantic layers[J].CAAI Transactions on Intelligent Systems,2015,10(2):178-186.[doi:10.3969/j.issn.1673-4785.201407009]
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Inference framework for activity recognition based on multiple semantic layers

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 2 Page number: 178-186 Column: 学术论文—智能系统 Public date: 2015-04-25
Title:
Inference framework for activity recognition based on multiple semantic layers
Author(s):
NIE Huirao;  TAO Linmi
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Keywords:
activity recognition; feature activity relation; environment context; semantic layer; multilayer inference framework
CLC:
TP301.6
DOI:
10.3969/j.issn.1673-4785.201407009
Abstract:
Human activity recognition is the core of the implementation of human-centered computing(HCC), whose nature is to acquire activities’ semanteme. The basic problem is the semantic gap between observable actions and human activities. They should be bridged by environment context based inference. In this paper, a method is proposed to model the environment context implicitly. Further, a novel semanteme multilayered activity inference framework was presented, which divided the inferring process into 2 stages. One stage used to acquire fuzzy semanteme and another one to acquire accurate semanteme. The feature set was divided into different subsets according to knowledge. The system extracts the corresponding features according to the current state and obtains the possible set of candidate activities that can instruct the system to update the current feature set. Update the features set and infer it, the process continues until the inference is completed. The modeling method and progressive inference framework proposed could handle the activity-recognition problem well. Implicitly modeling the environment context could improve the accuracy of activity recognition. The progressive framework can improve the efficiency by avoiding extracting all features indistinguishably, whose validity was proven in the data set.
References:
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