Towards complex activity recognition using a Bayesian network-based probabilistic generative framework
作者：
Highlights：
• A Bayesian network-based probabilistic generative framework is presented to address diversity and uncertainty in complex activity recognition.
• The framework introduces the Chinese restaurant process to explicitly characterize the unique configurations of a complex activity.
• An enhanced model is presented to characterize more temporal relational variabilities than the previous models over our framework.
• Our models significantly outperform the state-of-the-arts on three benchmark datasets with different challenges.
• Our approach is robust against the incomplete or incorrect observations of primitive events.
摘要
•A Bayesian network-based probabilistic generative framework is presented to address diversity and uncertainty in complex activity recognition.•The framework introduces the Chinese restaurant process to explicitly characterize the unique configurations of a complex activity.•An enhanced model is presented to characterize more temporal relational variabilities than the previous models over our framework.•Our models significantly outperform the state-of-the-arts on three benchmark datasets with different challenges.•Our approach is robust against the incomplete or incorrect observations of primitive events.
论文关键词：Activity recognition,Bayesian network,Complex activity,Probabilistic generative model,Temporal relation,Uncertainty
论文评审过程：Received 11 December 2016, Revised 17 February 2017, Accepted 23 February 2017, Available online 27 February 2017, Version of Record 22 April 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.02.028
