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Complex activity recognition is challenging due to the inherent uncertaintyand diversity of performing a complex activity. Normally, each instance of acomplex activity has its own configuration of atomic actions and their temporaldependencies. We propose in this paper an atomic action-based Bayesian modelthat constructs Allen's interval relation networks to characterize complexactivities with structural varieties in a probabilistic generative way: Byintroducing latent variables from the Chinese restaurant process, our approachis able to capture all possible styles of a particular complex activity as aunique set of distributions over atomic actions and relations. We also showthat local temporal dependencies can be retained and are globally consistent inthe resulting interval network. Moreover, network structure can be learned fromempirical data. A new dataset of complex hand activities has been constructedand made publicly available, which is much larger in size than any existingdatasets. Empirical evaluations on benchmark datasets as well as our in-housedataset demonstrate the competitiveness of our approach.

An Interval-Based Bayesian Generative Model for Human Complex Activity Recognition