Đang chuẩn bị liên kết để tải về tài liệu:
Báo cáo hóa học: " Research Article Efficient Adaptive Combination of Histograms for Real-Time 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 Efficient Adaptive Combination of Histograms for Real-Time Tracking | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2008 Article ID 528297 11 pages doi 10.1155 2008 528297 Research Article Efficient Adaptive Combination of Histograms for Real-Time Tracking F. Bajramovic 1 B. Deutsch 2 Ch. GraBI 2 and J. Denzler1 1 Department of Mathematics and Computer Science Friedrich-Schiller University Jena 07737 Jena Germany 2 Computer Science Department 5 University of Erlangen-Nuremberg 91058 Erlangen Germany Correspondence should be addressed to F. Bajramovic ferid.bajramovic@informatik.uni-jena.de Received 30 October 2007 Revised 14 March 2008 Accepted 12 July 2008 Recommended by Fatih Porikli We quantitatively compare two template-based tracking algorithms Hager s method and the hyperplane tracker and three histogram-based methods the mean-shift tracker two trust-region trackers and the CONDENSATION tracker. We perform systematic experiments on large test sequences available to the public. As a second contribution we present an extension to the promising first two histogram-based trackers a framework which uses a weighted combination of more than one feature histogram for tracking. We also suggest three weight adaptation mechanisms which adjust the feature weights during tracking. The resulting new algorithms are included in the quantitative evaluation. All algorithms are able to track a moving object on moving background in real time on standard PC hardware. Copyright 2008 F. Bajramovic 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. 1. INTRODUCTION Data driven real-time object tracking is still an important and in general unsolved problem with respect to robustness in natural scenes. For many high-level tasks in computer vision it is necessary to track a moving object in many cases on moving background in real time without having .