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

Complex activity recognition is challenging due to the inherent uncertainty and diversity of performing a complex activity. Normally, each instance of a complex activity has its own configuration of atomic actions and their temporal dependencies. In our previous work, we proposed an atomic action-based Bayesian model that constructs Allen's interval relation networks to characterize complex activities in a probabilistic generative way: By introducing latent variables from the Chinese restaurant process, our approach is able to capture all possible styles of a particular complex activity as a unique set of distributions over atomic actions and relations. However, a major limitation of our previous models is their fixed network structures, which may lead to an overtrained or undertrained model owing to unnecessary or missing links in a network. In this work, we present an improved model that network structures can be automatically learned from empirical data, allowing itself to characterize complex activities with structural varieties. In addition, a new dataset of complex hand activities has been constructed and made publicly available, which is much larger in size than any existing datasets. Empirical evaluations on benchmark datasets as well as our in-house dataset demonstrate the competitiveness of our approach.