Violence and horror video detection method based on MoSIFT and CSD features

A video detection and video technology, applied in the field of violent terrorist video detection algorithms, can solve problems such as different color space distributions

Active Publication Date: 2016-04-20
SHANGHAI JIAO TONG UNIV +1
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Problems solved by technology

For example, a window with a size of 8×8 pixels slides on the entire image and counts the color types that appear in the window. The CSD feature is extracted in the HMMD (Hue, Min, Max, Difference) color space. The main advantage is that it can distinguish the color histogram Pairs of images that are similar but have relatively different color space distributions

Method used

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  • Violence and horror video detection method based on MoSIFT and CSD features
  • Violence and horror video detection method based on MoSIFT and CSD features
  • Violence and horror video detection method based on MoSIFT and CSD features

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Embodiment Construction

[0070] The present invention will be described in detail below in conjunction with specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0071] Specifically, such as figure 1 Shown, according to the detection method of the violent terrorism video based on MoSIFT and CSD feature provided by the present invention, comprise the steps:

[0072] Step 1: Extract the MoSIFT features of the test video and the training video respectively;

[0073] Step 2: Reduce the number and dimension of the training video after extracting MoSIFT features;

[0074] Step 3: Cluster the MoSIFT features of the training video after the number and dimension ...

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Abstract

The invention provides a violence and horror video detection method based on MoSIFT (Motion ScaleInvariant Feature Transform) and CSD (Color Structure Descriptor) features, and the method comprises the steps: step1, calculating CSD of a video; step2, calculating a maximum dynamic density point of CSD features; step3, calculating a CSD fraction; step4, calculating MoSIFT of the video; step5, performing vertical dimensionality reduction for MoSIFT features; step6, performing horizontal dimensionality reduction for the video; step7, performing clustering for the MoSIFT features; step8, training SVM-1; step9, calculating a MoSIFT fraction; step10, training SVM-2; and step11, obtaining a classification result. According to the invention, the MoSIFT features and the CSD features are well-used, the complexity of the algorithm is reduced, and a relatively good detection effect can be obtained.

Description

technical field [0001] The present invention relates to a violent terrorism video detection algorithm, in particular to a violent terrorism video detection algorithm based on MoSIFT and CSD features. Background technique [0002] With the continuous development of the Internet, all kinds of video content disseminated through the Internet have become vast, and there are many violent and horror videos among them. Such videos are likely to have a bad psychological impact on minors. Therefore, it is necessary to classify and control videos on the Internet. The traditional method is to manually detect and review a large number of videos. The horror video detection method is very meaningful. [0003] There are many ways to detect violent and terrorist videos. Generally, audio signals and visual signals are used to identify abnormal sound information such as screams and explosions through audio signals, and images such as blood, darkness, and fighting are identified through visual...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V20/44G06V20/40G06V20/46G06V10/462G06F18/2411
Inventor 蒋兴浩孙锬锋倪俊郑辉王丹阳
Owner SHANGHAI JIAO TONG UNIV
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