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

•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.