Discriminative model selection using a modified Bayesian criterion: Application to trajectory modeling
Jacinto C. Nascimento, Jorge Salvador Marques, Mário A. T. Figueiredo · 2011
In this paper we introduce a novel method to determine the model order of a stochastic model for moving objects. The main assumption is that we make use of the knowledge that the obtained model is going to be used for some task, specifically, for trajectory classification. Particularly, the object motion is described by trajectories performed by the objects (e.g., pedestrians), during their motion, by representing them by a small and meaningful mixtures of vector fields. We present a discriminative method for model selection without resort to computationally expensive cross-validation procedures. The idea is, thus, to select the generative model achieving the best classification performance. Although the topic of application is video surveillance, the proposed method can easily be extended to other practical situations. Experiments with both synthetic and real data concerning pedestrian activities illustrate the performance of the proposed approach.