|Conference/Proceedings||IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS 2012)|
|Author(s)||Tobias Senst, Rubén Heras Evangelio, Ivo Keller, Thomas Sikora|
|Title||Clustering Motion for Real-Time Optical Flow based Tracking|
|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.
|Key words||Feature Tracking, Long-Term Trajectories, Optical Flow|