MODULE FOR ADJUSTING PARAMETERS OF ALGORITHMS FOR AUTOMATIC DETECTION AND TRACKING OF OBJECTS FOR OPTOELECTRONIC SYSTEMS

Abstract

In order to create an innovative module for automatic correction of algorithms for automatic
detection and tracking of objects with real-time training, a study of world experience in the field
of general-purpose automatic tracking with the ability to recognize the tracking object for use in
embedded computing devices of optoelectronic systems of promising robotic complexes was carried
out. Based on the conducted research, methods and approaches have been selected and tested
that allow with the greatest accuracy, while maintaining high computational efficiency, to provide
on-the-fly training of classifiers (online learning) without a priori knowledge of the type of tracking object and to ensure subsequent correction during tracking and detection of the original object
in case of its short-term loss. Such methods include a histogram of directional gradients – a descriptor
of key features based on the analysis of the distribution of brightness gradients of an object
image. Its use allows you to reduce the amount of information used without losing key data
about the object and increase the speed of image processing. The article substantiates the choice
of one of the classification algorithms in real time, which allows solving the problem of binary
classification - the method of support vectors. Due to the high speed of data processing and the
need for a small amount of initial training data to build a separating hyperplane, on the basis of
which the classification of objects takes place, this method is chosen as the most suitable for solving
the task. To implement online training, a modification of the support vector machine was chosen,
implementing stochastic gradient descent at each step of the algorithm – Pegasos. Another
auxiliary method is the clustering method of key points – this ensures an accelerated selection of
objects for classification and training. The authors of the study carried out the development and
semi-natural modeling of the proposed module, evaluated the effectiveness of its work in the tasks
of correcting and detecting the 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. In conclusion, proposals are presented to further improve the accuracy and probability
of detecting an object of interest by the developed algorithm, as well as to improve its performance
by optimizing calculations.