@article{ref1,
title="Learning to Recognize Video-Based Spatiotemporal Events",
journal="IEEE transactions on intelligent transportation systems",
year="2009",
author="Veeraraghavan, H. and Papanikolopoulos, N.p.",
volume="10",
number="4",
pages="628-638",
abstract="A key research issue in activity recognition in real-world applications, such as in intelligent transportation systems (ITS), is to automatically learn robust models of activities that require minimal human training. In this paper, we contribute a novel approach for learning sequenced spatiotemporal activities in outdoor traffic intersections. Concretely, by representing the activities as sequences of actions, we contribute a semisupervised learning algorithm that learns activities as complete stochastic context-free grammars (SCFGs), namely, the grammar structure and the parameters. Our approach has been implemented and tested on real-world scenes, and we present experimental results of the grammar learning and activity recognition applied to data collection and traffic monitoring applications using video data.<p />",
language="",
issn="1524-9050",
doi="10.1109/TITS.2009.2026440",
url="http://dx.doi.org/10.1109/TITS.2009.2026440"
}