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Scientific Publications

Clustering Motion for Real-Time Optical Flow based Tracking
Citation key 1366Senst2012
Author Tobias Senst and Rubén Heras Evangelio and Ivo Keller and Thomas Sikora
Title of Book IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS 2012)
Pages 410–415
Year 2012
Address Beijing, China
Month sep
Note ISBN: 978-1-4673-2499-1 DOI: 10.1109/AVSS.2012.20
Abstract The selection of regions or sets of points to track is a key task in motion-based video analysis, which has significant performance effects in terms of accuracy and computational efficiency. Computational efficiency is an unavoidable requirement in video surveillance applications. Well established methods, e.g., Good Features to Track, select points to be tracked based on appearance features such as cornerness, therefore neglecting the motion exhibited by the selected points. In this paper, we propose an interest point selection method that takes the motion of the tracked points into account in order to constrain the number of point trajectories needed. By defining pair-wise temporal affinities between trajectories and representing them in a minimum spanning tree, we achieve a very efficient clustering. The number of trajectories assigned to each motion cluster is adapted by initializing and removing tracked points by means of feed-back. Compared to the KLT tracker, we save up to 65% of the points to track, therefore gaining in efficiency while not scarifying accuracy.
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