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Study on feature extraction technology of real-time video acquisition based on deep CNN

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Abstract

In the process of image acquisition, the existing image-based real-time video acquisition system is susceptible to noise and distortion due to the influence of attitude, illumination and other conditions, which reduces the quality and stability of the acquired image, and thus makes it difficult to locate the image feature area. Therefore, the feature extraction technology of real-time video capture based on deep convolution neural network is proposed. Cut out high-quality images by locating reference points in feature connection areas, smooth each part of the image by using mean image filter, extract texture features by using convolution, transform, discrete cosine transform and statistical features, and replace random initialization weights with pre-trained models. In the process of model training and recognition, the methods of feature state division, image preprocessing and observation vector calculation are studied. The experimental results on ORL database verify the effectiveness of the image feature extraction method, which can meet the needs of current real-time video capture.

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Acknowledgements

This work is partially supported by the Natural Science Fund of China (under Grant 61401356), the Special Fund for Technological Innovation Guidance of Shaanxi Province (under grant 2020CGXNG-015), Youth Innovation Team Building Scientific Research Project of Shaanxi Province (under Grant 21JP106), and the Science and Technology Project of Xi’an (under Grant 2019KJWL07, 2020kjrc0104).

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Authors and Affiliations

  1. Shaanxi Key Laboratory of Surface Engineering and Remanufacturing, Xi’an University, Xi’an, 710065, Shaanxi, China

    Senlin Yang

  2. School of Mechanic & Material Engineering, Xi’an University, Xi’an, 710065, Shaanxi, China

    Senlin Yang

  3. Vertiv Technology Ltd., Xi’an, 710075, China

    Xin Chong

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  1. Senlin Yang
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  2. Xin Chong
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Correspondence to Senlin Yang.

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Yang, S., Chong, X. Study on feature extraction technology of real-time video acquisition based on deep CNN. Multimed Tools Appl 80, 33937–33950 (2021). https://doi.org/10.1007/s11042-021-11417-7

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  • DOI: https://doi.org/10.1007/s11042-021-11417-7

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