Image quality assessment for video stream recognition systems

Timofey S. Chernov; Nikita P. Razumnuy; Alexander S. Kozharinov; Dmitry P. Nikolaev; Vladimir V. Arlazarov

doi:10.1117/12.2309628

13 April 2018 Image quality assessment for video stream recognition systems

Timofey S. Chernov, Nikita P. Razumnuy, Alexander S. Kozharinov, Dmitry P. Nikolaev, Vladimir V. Arlazarov

Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106961U (2018) https://doi.org/10.1117/12.2309628
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria

Abstract

Recognition and machine vision systems have long been widely used in many disciplines to automate various processes of life and industry. Input images of optical recognition systems can be subjected to a large number of different distortions, especially in uncontrolled or natural shooting conditions, which leads to unpredictable results of recognition systems, making it impossible to assess their reliability. For this reason, it is necessary to perform quality control of the input data of recognition systems, which is facilitated by modern progress in the field of image quality evaluation. In this paper, we investigate the approach to designing optical recognition systems with built-in input image quality estimation modules and feedback, for which the necessary definitions are introduced and a model for describing such systems is constructed. The efficiency of this approach is illustrated by the example of solving the problem of selecting the best frames for recognition in a video stream for a system with limited resources. Experimental results are presented for the system for identity documents recognition, showing a significant increase in the accuracy and speed of the system under simulated conditions of automatic camera focusing, leading to blurring of frames.

Citation Download Citation

Timofey S. Chernov, Nikita P. Razumnuy, Alexander S. Kozharinov, Dmitry P. Nikolaev, and Vladimir V. Arlazarov "Image quality assessment for video stream recognition systems", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106961U (13 April 2018); https://doi.org/10.1117/12.2309628

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