Towards complex activity recognition using a Bayesian network-based probabilistic generative framework.

•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.