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Báo cáo hóa học: " Research Article AUTO GMM-SAMT: An Automatic Object Tracking System for Video Surveillance in Traffic Scenarios"

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 AUTO GMM-SAMT: An Automatic Object Tracking System for Video Surveillance in Traffic Scenarios | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2011 Article ID 814285 14 pages doi 10.1155 2011 814285 Research Article AUTO GMM-SAMT An Automatic Object Tracking System for Video Surveillance in Traffic Scenarios Katharina Quast EURASIP Member and Andre Kaup EURASIP Member Multimedia Communications and Signal Processing University of Erlangen-Nuremberg Cauerstr. 7 91058 Erlangen Germany Correspondence should be addressed to Katharina Quast quast@lnt.de Received 1 April 2010 Revised 30 July 2010 Accepted 26 October 2010 Academic Editor Carlo Regazzoni Copyright 2011 K. Quast and A. Kaup. 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. A complete video surveillance system for automatically tracking shape and position of objects in traffic scenarios is presented. The system called Auto GMM-SAMT consists of a detection and a tracking unit. The detection unit is composed of a Gaussian mixture model- GMM- based moving foreground detection method followed by a method for determining reliable objects among the detected foreground regions using a projective transformation. Unlike the standard GMM detection the proposed detection method considers spatial and temporal dependencies as well as a limitation of the standard deviation leading to a faster update of the mixture model and to smoother binary masks. The binary masks are transformed in such a way that the object size can be used for a simple but fast classification. The core of the tracking unit named GMM-SAMT is a shape adaptive mean shift- SAMT- based tracking technique which uses Gaussian mixture models to adapt the kernel to the object shape. GMM-SAMT returns not only the precise object position but also the current shape of the object. Thus Auto GMM-SAMT achieves good tracking results even if the object is performing