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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 A Feedback-Based Algorithm for Motion Analysis with Application to Object Tracking | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 86064 17 pages doi 10.1155 2007 86064 Research Article A Feedback-Based Algorithm for Motion Analysis with Application to Object Tracking Shesha Shah and P. S. Sastry Department of Electrical Engineering Indian Institute of Science Bangalore 560 012 India Received 1 December 2005 Revised 30 July 2006 Accepted 14 October 2006 Recommended by Stefan Winkler We present a motion detection algorithm which detects direction of motion at sufficient number of points and thus segregates the edge image into clusters of coherently moving points. Unlike most algorithms for motion analysis we do not estimate magnitude of velocity vectors or obtain dense motion maps. The motivation is that motion direction information at a number of points seems to be sufficient to evoke perception of motion and hence should be useful in many image processing tasks requiring motion analysis. The algorithm essentially updates the motion at previous time using the current image frame as input in a dynamic fashion. One of the novel features of the algorithm is the use of some feedback mechanism for evidence segregation. This kind of motion analysis can identify regions in the image that are moving together coherently and such information could be sufficient for many applications that utilize motion such as segmentation compression and tracking. We present an algorithm for tracking objects using our motion information to demonstrate the potential of this motion detection algorithm. Copyright 2007 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION Motion analysis is an important step in understanding a sequence of image frames. Most algorithms for motion analysis 1 2 essentially perform motion detection on consecutive image frames as input. One can broadly categorize them as correlation-based methods or gradient-based methods. Correlation-based methods try to establish .
