We are happy to announce that the following paper has been accepted to the
10th International Conference on Computer Vision Theory and Applications
(VISAPP)
that will take place in Berlin, on March 11 - 14, 2015:
A self-adaptive likelihood function for tracking with
particle filter
SĂ©verine Dubuisson (1), Myriam Robert-Seidowsky (2) and Jonathan
Fabrizio (2)
(1) CNRS, UMR 7222, ISIR, F-75005, Paris, France
(2) LRDE-EPITA, 14-16, rue Voltaire, F-94276, Le Kremlin
BicĂȘtre, France
Abstract:
The particle filter is known to be efficient for visual
tracking. However, its parameters are empirically fixed,
depending on the target application, the video sequences
and the context. In this paper, we introduce a new
algorithm which automatically adjusts ``on-line" two majors
of them: the correction and the propagation parameters. Our
purpose is to determine, for each frame of a video, the
optimal value of the correction parameter and to adjust the
propagation one to improve the tracking performance. On one
hand, our experimental results show that the common
settings of particle filter are sub-optimal. On another
hand, we prove that our approach achieves a lower tracking
error without needing tuning these parameters. Our adaptive
method allows to track objects in complex conditions
(illumination changes, cluttered background, etc.) without
adding any computational cost compared to the common usage
with fixed parameters.
--
Myriam Robert-Seidowsky - LRDE/EPITA
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