Latent Boosting for Action Recognition

In this paper we present LatentBoost, a novel learning algorithm for training models with latent variables in a boosting framework. This algorithm allows for training of structured latent variable models with boosting. The popular latent SVM framework allows for training of models with structured latent variables in a max-margin framework. LatentBoost provides an analogous capability for boosting algorithms. The effectiveness of this framework is highlighted by an application to human action recognition. We show that LatentBoost can be used to train an action recognition model in which the trajectory of a person is a latent variable. This model outperforms baselines on a variety of datasets.