VIDEO DATA QUALITY IMPROVEMENT METHODS AND TOOLS DEVELOPMENT FOR MOBILE VISION SYSTEMS

Abstract

Subject of study. The article proposes new methods of input and preliminary processing of video data for web- and specialized pi-cameras in monocular and stereo vision systems based on Raspberry Pi microcomputers to improve the quality of work of modern mobile vision systems. This approach is always relevant, since the design of modern vision systems constantly requires new non-trivial hardware, algorithmic and software solutions. Objectives. The goals are to compare quality indicators of the known methods of input and preliminary processing of video data in vision systems and to develop new methods and algorithms providing better speed, the necessary frame resolution and the independence of the frame brightness from changes in scene illumination while reading video data. Methods used. The paper formulates a comprehensive criterion for improving the quality of the Raspberry Pi microcomputer video input and preprocessing video data. Based on the accepted quality indicators (video input speed, resolution, and stability indicators of average brightness of the current frames of the received video stream), video input and preprocessing algorithms that satisfy the specified requirements are synthesized. This allows to find the optimal method for processing video data and to overcome the contradiction of reducing the input speed due to the need of increasing the resolution of video frames for each project. The created universal program for input and preliminary processing of these data allowed to obtain quantitative estimates of the effectiveness of the developed algorithms and formulate recommendations for their further use. All this allows you to significantly increase the efficiency of using Raspberry Pi microcomputers in modern mobile vision systems. The results obtained are the basis for the creation of a universal software product for high-speed input (in real time) and preliminary processing of video data for face detection and recognition systems, as well as stereo vision systems. Conclusions. The conducted experimental studies confirmed the efficiency and effectiveness of the proposed methods and algorithms for high-speed input of video data with different values of the resolution of the frame and the ability to adaptively adjust its brightness. Based on the created methods and algorithms, various options for its software implementation are proposed. This allows us to recommend the results for practical use. Prospects for further research include the expansion of the vector of criteria for assessing quality and features for optimizing video data, as well as the creation of new algorithms and various versions of programs based on them.