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

This paper presents a robust multi-cue approach to the integrated detection and tracking of pedestrians in cluttered urban environment. A novel spatio-temporal object representation is proposed that combines a generative shape model and a discriminative texture classifier, both composed of a mixture of pose-specific submodels. Shape is represented by a set of linear subspace models, an extension of Point Distribution Models, with shape transitions modeled by a first-order Markov process. Texture, i.e. the shape-normalized intensity pattern, is represented by a manifold implicitly delimited by a set of pattern classifiers, while texture transition is modeled by a random walk. Direct 3D measurements provided by a stereo system are furthermore 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 involving pedestrian detection and tracking from a moving vehicle demonstrate the benefit of the proposed approach.