Multi-person activity recognition is a challenging task due to the complex interactions between people and the multi-dimensionality of features. This paper proposes a hierarchical and observation decomposed hidden Markov model to classify multi-person activities. In order to give detailed descriptions of people's interactions by different feature scale, states of individual persons and states of interactions between people are separated. In addition, observations are decomposed into groups of subobservations to handle high dimensionality problem of feature space. This decomposition enhances the flexibility in feature selection which enables the combination of discrete and continuous features. Besides, this model has no limitations in terms of the number of persons. Experiments are successfully conducted with encouraging results. Activities of two persons and three persons are classified with good accuracies.
['Ping Guo', 'Zhenjiang Miao']
Multi-person activity recognition through hierarchical and observation decomposed HMM