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Pedestrian Detection and Tracking Using a Mixture of View-Based Shape–Texture Models

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3 Author(s)
Munder, S. ; Environ. Perception Dept., Daimler Res., Ulm ; Schnorr, C. ; Gavrila, D.M.

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.

Published in:

Intelligent Transportation Systems, IEEE Transactions on  (Volume:9 ,  Issue: 2 )