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.