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В.А. Бойков, В.Я. Колючкин
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ISSN 0236-3933. Вестник МГТУ им. Н.Э. Баумана. Сер. Приборостроение. 2017. № 5
ALGORITHM FOR OBJECT IMAGE AUTOMATIC TRACKING
V.A. Boykov
V.Ya. Kolyuchkin
vkoluch@bmstu.ruBauman Moscow State Technical University, Moscow, Russian Federation
Abstract
Keywords
Development of reliable and precise object image selection
algorithms, which are invariant to object image texture, back-
ground and noise parameters, and moreover provide real-time
image processing, is a relevant problem.
Random Ferns
me-
thod seems suitable for developing algorithms with similar
qualities. In this paper, we propose an object image processing
algorithm, which provides object image selection and deter-
mination of the position data and overall dimensions for the
selected object image during the object tracking. The proposed
algorithm is based on
Random Ferns
method, and requires
initial training. We carried out numerical experiments to
evaluate the efficiency of such algorithm for several types of
objects, whose images, with complicated image texture, were
exposed on the spatially-irregular background. Moreover, we
blurred images with white Gaussian noise in order to vary the
signal-to-noise ratio. Findings of the research show that the
object image selection algorithm, based on
Random Ferns
method, in case if the signal-to-noise ratio is greater than 10,
provides reliable object image selection, i. e. correct selection
probability is near to 100 %. The algorithm provides position
data and overall dimensions measurement error less than
three pixels. The algorithm provides high performance. The
average computation time didn’t exceed 5 ms even for selec-
ting image of the object with complicated texture, which is
exposed on spatially-irregular background. Consequently, it is
suitable for real-time working computer vision systems
Image processing, object image
selection, tracking, random ferns,
vision system, spatially-irregular
background, real-time working
Received 27.12.2016
© BMSTU, 2017
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