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Frameworking method for violence detection using spatiotemporal characteristic analysis of shading image based on deep learning
Frameworking method for violence detection using spatiotemporal characteristic analysis of shading image based on deep learning
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机译:基于深度学习的阴影图像时空特征分析的暴力检测框架方法
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摘要
The present invention relates to a framework for detecting violence using spatial and temporal characteristics analysis of deep learning-based shadow images, and to improve the ability and accuracy of detecting violence in images. To this end, the present invention is a violent detection framework that detects a violent characteristic of an image by detecting a feature point of violence in an input image composed of video frames provided from a video camera or a video file. Step 1, extracting the 2D-based Y-frame black and white image by excluding the red (R), green (G), and blue (B) from each separated frame image, and the extracted 2D-based Y frame monochrome image A third step of sequentially accumulating a large number of 3D environments and converting them into Y-frame black and white images in a 3D environment, and extracting and accumulating frames of equal layers among the Y-frame monochrome images in the converted 3D environment to perform image convolution. Including the 4th step of deriving the desired detection scene using 3*3*3 filters, network-weighted and time-space-optimized video is created and applied to the algorithm to apply the feature points of violence to specific layers in the video convolution process. By continuously remembering and re-learning, it improves the violent detection ability and accuracy of the image, enables analysis regardless of the length of the analysis frame, and enables analysis of continuous behavior.
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