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báo cáo hóa học:" Research Article Motion Pattern Extraction and Event Detection for Automatic Visual Surveillance"

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 Motion Pattern Extraction and Event Detection for Automatic Visual Surveillance | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2011 Article ID 163682 15 pages doi 10.1155 2011 163682 Research Article Motion Pattern Extraction and Event Detection for Automatic Visual Surveillance Yassine Benabbas Nacim Ihaddadene and Chaabane Djeraba LIFL UMR CNRS 8022 - Universite Lillel TELECOM Lillel 59653 Villeneuve d Ascq Cedex France Correspondence should be addressed to Yassine Benabbas yassine.benabbas@lifl.fr Received 1 April 2010 Revised 30 November 2010 Accepted 13 December 2010 Academic Editor Luigi Di Stefano Copyright 2011 Yassine Benabbas 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. Efficient analysis of human behavior in video surveillance scenes is a very challenging problem. Most traditional approaches fail when applied in real conditions and contexts like amounts of persons appearance ambiguity and occlusion. In this work we propose to deal with this problem by modeling the global motion information obtained from optical flow vectors. The obtained direction and magnitude models learn the dominant motion orientations and magnitudes at each spatial location of the scene and are used to detect the major motion patterns. The applied region-based segmentation algorithm groups local blocks that share the same motion direction and speed and allows a subregion of the scene to appear in different patterns. The second part of the approach consists in the detection of events related to groups of people which are merge split walk run local dispersion and evacuation by analyzing the instantaneous optical flow vectors and comparing the learned models. The approach is validated and experimented on standard datasets of the computer vision community. The qualitative and quantitative results are discussed. 1. Introduction In the recent years there has .