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Violence detection based on three-stream convolutional networks

机译:基于三流卷积网络的暴力检测

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Violence detection in videos is a challenging task which has gotten much attention in the research community. In this paper, we propose a three-stream network framework for violence detection in binocular stereo vision. To capture the complementary information from the video we adopt the appearance, motion and depth information. The spatial part, we use the RGB as the individual frame appearance. Then, we use the sparse stereo matching method to extract the feature points and obtain the vision disparity of the point. The 3D coordinates of the points are calculated through the standard 3D measurement theory. The 3D motion vector conveys the movement of the camera and the objects as the motion information. Besides, the depth information flow is the third input of the network which can improved recognition rate.
机译:视频中的暴力检测是一个具有挑战性的任务,在研究界中得到了很多关注。在本文中,我们向双目立体声视觉中提出了一种三流网络的暴力检测框架。要从视频中捕获互补信息,我们采用外观,运动和深度信息。空间部分,我们将RGB用作单独的框架外观。然后,我们使用稀疏的立体声匹配方法来提取特征点并获得该点的视觉视差。点的3D坐标通过标准的3D测量理论计算。 3D运动矢量将相机和对象的移动传送为运动信息。此外,深度信息流是网络的第三输入,其能够提高识别率。

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