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.