Learning structures of interval-based Bayesian networks in probabilistic generative model for human complex activity recognition

作者：

Highlights：

• A family of Bayesian network-based probabilistic generative models is presented to address diversity and uncertainty in complex activity recognition.

• The network structure in our improved model is learned from empirical data to characterize the inherent structural variability in complex activities.

• A new complex hand activity dataset is made publicly available dedicated to the purpose of complex activity recognition.

• Experimental results suggest our improved model outperforms existing state-of-the-arts by a large margin.

摘要

•A family of Bayesian network-based probabilistic generative models is presented to address diversity and uncertainty in complex activity recognition.•The network structure in our improved model is learned from empirical data to characterize the inherent structural variability in complex activities.•A new complex hand activity dataset is made publicly available dedicated to the purpose of complex activity recognition.•Experimental results suggest our improved model outperforms existing state-of-the-arts by a large margin.

论文关键词：Complex activity recognition,Structure learning,Bayesian network,Interval,Probabilistic generative model,American Sign Language dataset

论文评审过程：Received 19 September 2017, Revised 1 February 2018, Accepted 24 April 2018, Available online 25 April 2018, Version of Record 16 May 2018.

论文官网地址：https://doi.org/10.1016/j.patcog.2018.04.022