A Test to Determine the Multivariate Normality of a Data Set
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Bayesian Approaches to Gaussian Mixture Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimum-Entropy Data Partitioning Using Reversible Jump Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Unsupervised Parameterisation of Gaussian Mixture Models
CCIA '02 Proceedings of the 5th Catalonian Conference on AI: Topics in Artificial Intelligence
Wormholes in Shape Space: Tracking through Discontinuous Changes in Shape
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Probabilistic spatio-temporal 2d-model for pedestrian motion analysis in monocular sequences
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
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Many proposals to generate shape models for tracking applications are based on a linear shape model, and a constraint that delimits the parameter values which generate feasible shapes. In this paper we introduce a novel approach to generate such models automatically. Given a training set, we determine the linear shape model as classical approaches, and model its associated constraint using a Gaussian Mixture Model, which is fully parameterized by a presented algorithm. Then, from this model we generate a collection of linear shape models of lower dimensionality, each one constrained by a single Gaussian model. This set of models represents better the training set, reducing the computational cost of tracking applications. To compare our proposal with the usual one, a comparison measure is defined, based on the Bayesian Information Criterion. Both modeling strategies are analyzed in a pedestrian tracking application, where our proposal claims to be more appropriate.