@article{ref1,
title="Pedestrian Detection and Tracking Using a Mixture of View-Based Shape–Texture Models",
journal="IEEE transactions on intelligent transportation systems",
year="2008",
author="Munder, S. and Schnorr, C. and Gavrila, D.m.",
volume="9",
number="2",
pages="333-343",
abstract="This paper presents a robust multicue approach to the integrated detection and tracking of pedestrians in a cluttered urban environment. A novel spatiotemporal object representation is proposed, which combines a generative shape model and a discriminative texture classifier, both of which are composed of a mixture of pose-specific submodels. Shape is represented by a set of linear subspace models, which is an extension of point distribution models, with shape transitions being modeled by a first-order Markov process. Texture, i.e., the shape-normalized intensity pattern, is represented by a manifold that is implicitly delimited by a set of pattern classifiers, whereas texture transition is modeled by a random walk. Direct 3-D measurements that are provided by a stereo system are further incorporated into the observation density function. We employ a Bayesian framework based on particle filtering to achieve integrated object detection and tracking. Large-scale experiments that involve pedestrian detection and tracking from a moving vehicle demonstrate the benefit of the proposed approach.<p />",
language="",
issn="1524-9050",
doi="10.1109/TITS.2008.922943",
url="http://dx.doi.org/10.1109/TITS.2008.922943"
}