OBJECT DETECTION ALGORITHM FOR OPTOELECTRONIC SYSTEMS WITH ONLINE LEARNING
Abstract
In order to create a new algorithm for automatic detection of objects with real-time training, a
study of the world scientific groundwork in the field of general-purpose automatic tracking with the
ability to recognize a tracked object with the potential for application in embedded computing systems
of optoelectronic systems of promising robotic complexes was carried out. Based on the conducted
research, methods and approaches were selected and tested that allow, with the greatest accuracy,
while maintaining high computational efficiency, to provide training of classifiers on the fly
(online learning) without a priori knowledge of the type of tracking object and to ensure the subsequent
detection of the original object in the event of its short-term loss. Such methods include a histogram
of oriented gradients – a descriptor of key features based on the analysis of the distribution of
the brightness gradients of the object image. Its use allows you to reduce the amount of information
used without losing key data about the object and to increase the speed of image processing. The
article substantiates the choice of one of the real-time classification algorithms that allows solving
the problem of binary classification – the support vector machine. Due to the high speed of data processing
and the need for a small amount of initial training data to construct a separating hyperplane,
on the basis of which the classification of objects is done, this method is chosen as the most suitable
for solving the problem. For online training, a modification of the support vector machine method
was chosen, which implements stochastic gradient descent at each step of the algorithm – Pegasos.
The authors of the study carried out the development and semi-natural modeling of the selected algorithm,
evaluated the effectiveness of its work in the tasks of detecting an object of interest in real time
with preliminary online training in the process of tracking the object. The developed algorithm has
shown high efficiency in solving the problem and is planned to be implemented as part of a special
software for optoelectronic systems of advanced robotic systems. In the conclusion, proposals are
presented to further improve the accuracy and probability of the object detection by the developed
algorithm, as well as for improving its performance by optimizing calculations.
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