Mining intricate temporal rules for recognizing complex activities of daily living under uncertainty
作者：
Highlights：
• A probabilistic semantic-based framework is presented to explicitly perform diverse inference tasks.
• The framework combines Markov logic networks with 15 relations between activities.
• Advanced pattern mining techniques are introduced to learn intricate temporal rules.
• Performances can be improved by logical reasoning with the mined rules under uncertainty.
• Our approach is robust to the incomplete or incorrect observations of intervals.
摘要
Highlights•A probabilistic semantic-based framework is presented to explicitly perform diverse inference tasks.•The framework combines Markov logic networks with 15 relations between activities.•Advanced pattern mining techniques are introduced to learn intricate temporal rules.•Performances can be improved by logical reasoning with the mined rules under uncertainty.•Our approach is robust to the incomplete or incorrect observations of intervals.
论文关键词：Complex activity recognition,Propositional logic rule,Semantics,Pattern mining,Markov logic network,Uncertainty,Intricate relation
论文评审过程：Received 17 September 2015, Revised 2 April 2016, Accepted 14 July 2016, Available online 16 July 2016, Version of Record 1 August 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.07.024
