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

• Published in: IEEE transactions on intelligent transportation systems Vol. 9; no. 2; pp. 333 - 343
• Main Authors: Munder, S., Schnorr, C., Gavrila, D.M.
• Format: Journal Article
• Language: English
• Published: Piscataway, NJ IEEE 01.06.2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Artificial intelligence; Bayesian methods; Computer science; control theory; systems; Density functional theory; Density measurement; Economic models; Exact sciences and technology; Filtering; Markov processes; Object detection; Particle filtering; Particle tracking; Pattern recognition. Digital image processing. Computational geometry; pedestrian detection; Robustness; Shape; shape model; Spatiotemporal phenomena; Studies; texture classification; tracking; Particle filtering; shape model; texture classification; pedestrian detection; tracking; Markov process; Tracking; Image processing; Temporal databases; Pedestrian; Urban area; Cluttered environment; Modeling; Texture; Linear model; Direct method; Vector space; Classification; Spatial database; Bayes estimation; Spatial data structures; Target tracking; Filtering; Urban environment; Point shape; Random walk; Object detection; Density function; Moving body
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2008.922943

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.