STRUCTURE OF AIDED CLASSIFICATION OF GROUND OBJECTS BY VIDEO OBSERVATION

Authors

  • M. P. Mukhina National Aviation University, Kyiv
  • I. V. Barkulova National Aviation University, Kyiv

DOI:

https://doi.org/10.18372/1990-5548.54.12339

Keywords:

Feature vector, descriptive space, aided classification, Bayesian segmentation

Abstract

Analysis of classification structure by video observation has been done. It was formulated, that for feature extraction and their classification, normalized hypothesis for object feature detection, taking into account camera orientation and flight height, have being obtained. The system with aided classification based on probabilistic models, such as Bayesian classifier and Markov chain model, is proposed. The applied algorithm was used for detection by only two features related to Binary Large Objects (BLOB) analyses. Classification was done by two main feature parameters: area and center of mass.  Feature vector contains the most informative components and allows the minimization of decision risks. Results have proven the reliability of classification during a number of video frames in the condition of non-full data descriptive space.

Author Biographies

M. P. Mukhina, National Aviation University, Kyiv

Aviation Computer-Integrated Complexes Department, Educational & Research Institute of Information and Diagnostic Systems

Doctor of Engineering Science. Professor

I. V. Barkulova, National Aviation University, Kyiv

Aviation Computer-Integrated Complexes Department, Educational & Research Institute of Information and Diagnostic Systems

Post-graduate student

References

Teutsch, W. Krüger, and N. Heinze, “Detection and classification of moving objects from UAVs with optical sensors,” SPIE Defense, Security, and Sensing. 2011, pp. 80501J–80501J.

Syriamkin, and V. Shidlovkiy, Correlation-extremal radio navigation systems, Tomsk izdatelstvo, 2010, 316 p.

Javed, and M. Shah, “Tracking and object classification for automated surveillance,” in European Conference on Computer Vision, Springer, Berlin, Heidelberg, 2002, pp. 343–357.

Jianzhuang, L. Wenqing, and T. Yupeng, “Automatic thresholding of gray-level pictures using two-dimension Otsu method,” in IEEE International Conference on Circuits and Systems, 1991, Conference Proceedings, China, 1991, pp. 325–327.

Hassaballah, A. Abdelmgeid, and H. Alshazly, “Image Features Detection, Description and Matching,” Image Feature Detectors and Descriptors.” Springer International Publishing, 2016, pp. 11-45.

Chen, Y. Lin, and T. Y. Chen, “Intelligent vehicle counting method based on blob analysis in traffic surveillance,” in IEEE Second International Conference on Innovative Computing, Information and Control, 2007, pp. 238–238.

Piccardi, “Background subtraction techniques: a review,” in IEEE international conference on Systems, man and cybernetics, 2004, vol. 4, pp. 3099–3104.

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Issue

Vol. 4 No. 54 (2017)

Section

MATHEMATICAL MODELING OF PROCESSES AND SYSTEMS

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