tailieunhanh - Báo cáo hóa học: " Research Article Integrating the Projective Transform with Particle Filtering for Visual Tracking"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Integrating the Projective Transform with Particle Filtering for Visual Tracking | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2011 Article ID 839412 11 pages doi 2011 839412 Research Article Integrating the Projective Transform with Particle Filtering for Visual Tracking P. L. M. Bouttefroy 1 A. Bouzerdoum 1 S. L. Phung 1 and A. Beghdadi2 1School of Electrical Computer Telecom. Engineering University of Wollongong Wollongong NSW2522 Australia 2L2TI Institut Galilee Universite Paris I3 93430 Villetaneuse France Correspondence should be addressed to P. L. M. Bouttefroy Received 9 April 2010 Accepted 26 October 2010 Academic Editor Carlo Regazzoni Copyright 2011 P. L. M. Bouttefroy et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 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 ofthe 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