Tracking Deformable Objects in the Plane Using an Active Contour Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking regions of human skin through illumination changes
Pattern Recognition Letters - Special issue: Colour image processing and analysis
Dynamical Gaussian mixture model for tracking elliptical living objects
Pattern Recognition Letters
Unsupervised signal restoration using hidden Markov chains with copulas
Signal Processing
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
Mean shift blob tracking with kernel histogram filtering and hypothesis testing
Pattern Recognition Letters
Estimation of generalized mixtures and its application in image segmentation
IEEE Transactions on Image Processing
Face segmentation using skin-color map in videophone applications
IEEE Transactions on Circuits and Systems for Video Technology
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To track objects in video sequences, many studies have been done to characterize the target with respect to its color distribution. Most often, the Gaussian mixture model (GMM) is used to represent the object color density. In this paper, we propose to extend the normality assumption to more general families of distributions issued from the Pearson’s system. Precisely, we propose a method called Pearson mixture model (PMM), used in conjunction with Gaussian copula, which is dynamically updated to adapt itself to the appearance change of the object during the sequence. This model is combined with Kalman filtering to predict the position of the object in the next frame. Experimental results on gray-level and color video sequences show tracking improvements compared to classical GMM. Especially, the PMM seems robust to illumination variations, pose and scale changes, and also to partial occlusions, but its computing time is higher than the computing time of GMM.