Semi-Supervised Learning of Switched Dynamical Models for Classification of Human Activities in Surveillance Applications

This work introduces a semi-supervised approach for learning generative models for classification/recognition of human trajectories, with application to surveillance. The classifier is based on switched dynamical models, with each model describing a specific motion regime. We present a semi-supervised modified version of the classical Baum-Welch algorithm, which is able to take into account a subset of known model labels. The experimental results reported, using both synthetic and real data, show that the classifier learned with semi-supervision leads to a higher classification accuracy than the fully unsupervised version, thus validating the proposed approach.