tailieunhanh - Báo cáo hóa học: " Research Article Efficient Data Association in Visual Sensor Networks with Missing Detection"

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 Data Association in Visual Sensor Networks with Missing Detection | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 176026 25 pages doi 2011 176026 Research Article Efficient Data Association in Visual Sensor Networks with Missing Detection Jiuqing Wan and Qingyun Liu Department of Automation Beijing University of Aeronautics and Astronautics Beijing 100191 China Correspondence should be addressed to Jiuqing Wan wanjiuqing@ Received 26 October 2010 Revised 16 January 2011 Accepted 18 February 2011 Academic Editor M. Greco Copyright 2011 J. Wan and Q. Liu. 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. One of the fundamental requirements for visual surveillance with Visual Sensor Networks VSN is the correct association of camera s observations with the tracks of objects under tracking. In this paper we model the data association in VSN as an inference problem on dynamic Bayesian networks DBN and investigate the key problems for efficient data association in case of missing detection. Firstly to deal with the problem of missing detection we introduce a set of random variables namely routine variables into the DBN model to describe the uncertainty in the path taken by the moving objects and propose the high-order spatio-temporal model based inference algorithm. Secondly for the problem of computational intractability of exact inference we derive two approximate inference algorithms by factorizing the belief state based on the marginal and conditional independence assumptions. Thirdly we incorporate the inference algorithm into EM framework to make the algorithm suitable for the case when object appearance parameters are unknown. Simulation and experimental results demonstrate the effect of the proposed methods. 1. Introduction Consisting of a large number of cameras with nonoverlapping field of view .