Integrating the Projective Transform with Particle Filtering for Visual Tracking. (3rd November 2010)

• Record Type: Journal Article
• Authors: Bouttefroy, P. L. M.; Bouzerdoum, A.; Phung, S. L.; Beghdadi, A.
• Other Names: Regazzoni Carlo Academic Editor.
• Is Part Of: EURASIP journal on image and video processing. Volume 2011(2011)
• Publication Date: 2010-11-03
• Subjects: Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; Traitement d'images; Vidéo numérique; Digital video; Image processing -- Digital techniques; Periodicals; Electronic journal; Electronic journals; 621.367
• DOI: 10.1155/2011/839412
• Languages: English
• ISSNs: 1687-5176

Abstract

Abstract : This paper presents the projective particle filter, a Bayesian filtering technique integrating the projective transform, which describes the distortion of vehicle trajectories on the camera plane. The characteristics inherent to traffic monitoring, and in particular the projective transform, are integrated in the particle filtering framework in order to improve the tracking robustness and accuracy. It is shown that the projective transform can be fully described by three parameters, namely, the angle of view, the height of the camera, and the ground distance to the first point of capture. This information is integrated in the importance density so as to explore the feature space more accurately. By providing a fine distribution of the samples in the feature space, the projective particle filter outperforms the standard particle filter on different tracking measures. First, the resampling frequency is reduced due to a better fit of the importance density for the estimation of the posterior density. Second, the mean squared error between the feature vector estimate and the true state is reduced compared to the estimate provided by the standard particle filter. Third, the tracking rate is improved for the projective particle filter, hence decreasing track loss.