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Image quality assessment for video stream recognition systems

机译：视频流识别系统的图像质量评估

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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 modem 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.
机译：识别和机器视觉系统早已在许多学科中广泛用于使生活和工业的各种过程自动化。光学识别系统的输入图像可能会遭受大量不同的失真，尤其是在不受控制或自然的拍摄条件下，这会导致识别系统的结果无法预测，从而无法评估其可靠性。因此，有必要对识别系统的输入数据进行质量控制，这在图像质量评估领域中随着调制解调器的进步而变得容易。在本文中，我们研究了设计具有内置输入图像质量估计模块和反馈的光学识别系统的方法，为此引入了必要的定义并构建了描述此类系统的模型。通过为资源有限的系统解决在视频流​​中选择最佳帧进行识别的问题，来举例说明该方法的效率。提出了用于身份文件识别系统的实验结果，表明在自动照相机聚焦的模拟条件下，系统的准确性和速度有了显着提高，从而导致帧模糊。

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