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A Motion Correspondence Algorithm based on Point Series Similarity  

Eom, Ki-Yeol (성균관대학교 정보통신공학부)
Jung, Jae-Young (동양대학교 컴퓨터정보전학과)
Kim, Moon-Hyun (성균관대학교 정보통신공학부)
Publication Information
Journal of KIISE:Software and Applications / v.37, no.4, 2010 , pp. 305-310 More about this Journal
Abstract
In this paper, we propose a heuristic algorithm for motion correspondence based on a point series similarity. A point series is a sequence of points which are sorted in the ascending order of their x-coordinate values. The proposed algorithm clusters the points of a previous frame based on their local adjacency. For each group, we construct several potential point series by permuting the points in it, each of which is compared to the point series of the following frame in order to match the set of points through their similarity based on a proximity constraint. The longest common subsequence between two point series is used as global information to resolve the local ambiguity. Experimental results show an accuracy of more than 90% on two image sequences from the PETS 2009 and the CAVIAR data sets.
Keywords
motion correspondence; object tracking; series similarity; trajectory; clustering; longest common subsequence;
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