Video Recognition of Government Community Management Cases Based on Partial Differential Equation Method

Yumeng Sun

Advances in Mathematical Physics, 2021, vol. 2021, issue 1

Abstract: With the development of urban economic construction and urban planning, higher requirements are put forward for the government community in the corresponding community management, community service, and other related things. As an important technical means to assist the government and community in management, video recognition technology plays an important role in the accurate management and service of the government and community. Traditional algorithms based on partial differential equations will destroy image edges and image details in video recognition. Based on this, this paper improves the traditional partial differential equation algorithm of image recognition, selects the GAC model based on image segmentation in the main function, and innovatively optimizes the stop function of its equation function, so as to improve the effect of community case image segmentation. In the image smoothing layer, this paper innovatively selects the second derivative based on image processing as the inherent feature of image recognition, so as to solve the rough problem of image edge and improve the processing efficiency of the algorithm. In order to further maintain the details of the relevant images of community cases, this paper integrates the Gaussian curvature driving function on the improved partial differential equation algorithm, so as to protect the details of the smooth region of the relevant recognition video and solve the disadvantages of the traditional algorithm. The experimental results show that the improved partial differential equation algorithm proposed in this paper improves the accuracy of video recognition by about 5% compared with the traditional algorithm. At the same time, the new algorithm can well ensure the detail integrity of the recognized video.

Date: 2021
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https://doi.org/10.1155/2021/5685311

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jnlamp:v:2021:y:2021:i:1:n:5685311

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