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

  V. А. Tupikov, V. А. Pavlova, А.I. Lizin, P.А. Gessen

  71-81

  2022-04-20

  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.

• CORRELATIONAL SUPPORT ALGORITHM WITH REAL-TIME LEARNING

  V. А. Tupikov, V. А. Pavlova, А.Y. Gagarina, P. А. Gessen, А.I. Lizin, М. V. Sozinova

  2022-04-21

  Abstract ▼

  In order to develop a stable algorithm for automatic detection and tracking of nondeterministic
  objects with real-time learning for embedded computing systems with optoelectronic
  devices, within the framework of this work, a study and analysis of the existing world scientific and
  technical experience in the field of automatic tracking algorithms for general purposes was carried
  out. The article shows that the most stable modern automatic tracking algorithms are a system
  that makes a decision about the current position, size and other parameters of the tracked
  image based on the model being trained. The authors of the study identified the most effective of
  the applied basic algorithms suitable for use in embedded computing systems of robotic complexes,
  and developed a new algorithm for automatic detection and maintenance of non-deterministic
  objects. A semi-natural testing of the developed algorithm was carried out and its effectiveness
  was evaluated in solving problems not only of automatic tracking of objects, but also problems of
  automatic detection of objects using several reference images. In conclusion, proposals are presented
  for further improving the accuracy of the developed algorithm and for its optimization and
  implementation in the special software of on-board computer systems of aircraft.