基于融合特征的网络不良视频识别方法研究  

Research on network bad video recognition based on features

作　　者：陈绣瑶[1] Chen Xiuyao(Guangdong Women's Plytechnic College,Guangzhou,Guangdong 511450,China)

机构地区：[1]广东女子职业技术学院应用设计系,广东广州511450

出　　处：《计算机时代》2020年第11期19-22,共4页Computer Era

基　　金：广州市哲学社会科学发展“十三五”规划课题“基于融合特征的网络不良视频识别方法研究”(2017GZGJ10)。

摘　　要：为了提高视频的识别检测速度,提出基于融合特征的网络不良视频识别框架。先分离视频音频,利用MFCC特征匹配筛查部分恐怖视频,减少视频图像提取、识别总量,以达到提高检测速度目的;再通过OpenCV视觉软件库,结合颜色直方图+SURF算法进行视频镜头边界检测及MoSIFT特征、颜色信息等其他视频特征的检测,在保证识别准确率的基础上进一步提升视频检测速率。In order to improve the video recognition ability and detection speed,this paper proposes a network bad video recognition framework based on features.Firstly,the video and audio are separated,and MFCC feature matching is used to screen some horror videos to reduce the total amount of video image for extraction and recognition,so as to improve the detection speed.In addition,by using OpenCV,combining with colour histogram and SURF algorithm,video lens boundary detection,and the detection of MoSIFT feature and motion,colour information and other video features are carried out to further improve video detection rate on the basis of ensuring recognition accuracy.

关 键 词：MFCC特征 SURF特征 MoSIFT特征 镜头边界检测 OPENCV 

分 类 号：TP37[自动化与计算机技术—计算机系统结构]