Pearson-based mixture model for color object tracking

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.

• Type: Journal
• Year: 2008
• Where: MVA
• Authors: William Ketchantang, Stéphane Derrode, Lionel Martin, Salah Bourennane
• Keywords: Gaussian Mixture Model, Mixture Model, MVA 2007, MVA 2008, Video Sequences