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 Feature Classification for Robust Shape-Based Collaborative Tracking and Model Updating | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2008 Article ID 274349 21 pages doi 2008 274349 Research Article Feature Classification for Robust Shape-Based Collaborative Tracking and Model Updating M. Asadi F. Monti 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 M. Asadi asadi@ Received 14 November 2007 Revised 27 March 2008 Accepted 10 July 2008 Recommended by Fatih Porikli A new collaborative tracking approach is introduced which takes advantage of classified features. The core of this tracker is a single tracker that is able to detect occlusions and classify features contributing in localizing the object. Features are classified in four classes good suspicious malicious and neutral. Good features are estimated to be parts of the object with a high degree of confidence. Suspicious ones have a lower yet significantly high degree of confidence to be a part of the object. Malicious features are estimated to be generated by clutter while neutral features are characterized with not a sufficient level of uncertainty to be assigned to the tracked object. When there is no occlusion the single tracker acts alone and the feature classification module helps it to overcome distracters such as still objects or little clutter in the scene. When more than one desired moving objects bounding boxes are close enough the collaborative tracker is activated and it exploits the advantages of the classified features to localize each object precisely as well as updating the objects shape models more precisely by assigning again the classified features to the objects. The experimental results show successful tracking compared with the collaborative tracker that does not use the classified features. Moreover more precise updated object shape models will be shown. Copyright 2008 M. Asadi et al. This is an .
