COVARIANCE TRACKING WITH FORGETTING FACTOR AND RANDOM SAMPLING
2011 ◽
Vol 19
(03)
◽
pp. 547-558
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Keyword(s):
Covariance matching is an excellent algorithm of target tracking. In this paper, forgetting factor and random sampling methods are proposed to improve the robustness and efficiency of covariance tracking. First, a distance function between covariance matrixes is weighted by using a forgetting factor based on a fuzzy membership function to overcome the disturbances from similar targets. Then a random sampling method is applied to reduce the computing time in covariance matching and to facilitate real-time object tracking. Experiment results show that the algorithm proposed in this paper can effectively mitigate the clutter and occlusion problems at a high computing speed.
2015 ◽
Vol 5
(1)
◽
pp. 35-68
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2009 ◽
Vol 4
(2)
◽
pp. 75-81
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Keyword(s):
2017 ◽
Vol 5
(6)
◽
2014 ◽
Vol 25
(05)
◽
pp. 1440007
◽
2021 ◽