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Báo cáo hóa học: "Research Article Tracking Using Continuous Shape Model Learning in the Presence of Occlusion"

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 Tracking Using Continuous Shape Model Learning in the Presence of Occlusion | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 250780 23 pages doi 10.1155 2008 250780 Research Article Tracking Using Continuous Shape Model Learning in the Presence of Occlusion M. Asadi and C. S. Regazzoni Department of Biophysical and Electronic Engineering University of Genoa Via All Opera Pia 11a 16145 Genoa Italy Correspondence should be addressed to C. S. Regazzoni carlo@dibe.unige.it Received 1 April 2007 Revised 2 October 2007 Accepted 17 January 2008 Recommended by Frank Ehlers This paper presents a Bayesian framework for a new model-based learning method which is able to track nonrigid objects in the presence of occlusions based on a dynamic shape description in terms of a set of corners. Tracking is done by estimating the new position of the target in a multimodal voting space. However occlusion events and clutter may affect the model learning leading to a distraction in the estimation of the new position of the target as well as incorrect updating of the shape model. This method takes advantage of automatic decisions regarding how to learn the model in different environments by estimating the possible presence of distracters and regulating corner updating on the basis of these estimations. Moreover by introducing the corner feature vector classification the method is able to continue learning the model dynamically even in such situations. Experimental results show a successful tracking along with a more precise estimation of shape and motion during occlusion events. Copyright 2008 M. Asadi and C. S. Regazzoni. 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. 1. INTRODUCTION In security surveillance issues tracking methods may be classified into two main groups feature-based and modelbased. In the model-based category learning and .