Abstract
Tins paper aims at addressing the occlusion problem, which is the challenging
part iii a video tracking system. Our algorithm is composed of two steps: loca
tion and recognition. In the location step, we first apply a non-linear regression
after segnientation to model the human body silhouette. Afterwards, we identify
peak points of the regressioli curve which are then used as candidate positions
of occluded objects. hi the recognition step, we find the correct correspondence
between candidate models and object color models stored prior to occlusion, so
that we cast derive tile position of each person during occlusion. We have com
pared our tracking method with two other popular tracking algorithms: meanshift
and particle-filter. Experimental results show that tile correctness of our
method is much lugher than the mean-shift algorithm and basically the same as
a particle-filter, however, with the major benefit of being a factor of 10-20 faster
in computing.
Original language | English |
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Title of host publication | Proceedings of the 29th Symposium on Information Theory in the Benelux, May 29-30, 2008, Leuven, Belgium |
Place of Publication | Leuven |
Publisher | IMEC |
Pages | 271-278 |
ISBN (Print) | 978-90-9023135-8 |
Publication status | Published - 2010 |