Citation

Munder S, Schnorr C, Gavrila DM. IEEE Trans. Intel. Transp. Syst. 2008; 9(2): 333-343.

Copyright

(Copyright © 2008, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2008.922943

PMID

unavailable

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.