Discriminative model selection for object motion recognition
A central issue in mixture-type models is the determination of a suitable number of components that best suits the observed data. In this paper, we address this issue in the context of trajectory classification based on mixtures of motion vector fields. We adopt a discriminative criterion for choosing among alternative models for each class, based on the classification accuracy on a held out dataset. The key idea is that we make use of the knowledge that the obtained model is going to be used for a specific task: classification. Experiments with both synthetic and real data concerning pedestrian activity classification illustrate the performance of the adopted criterion.