众包直播中精彩片段的自动化识别

Automatic Content Highlight Detection in Crowdsourced Live Video Streaming

近年来,基于众包的视频直播平台逐渐兴起,以其丰富的观众-主播交互机制吸引广大用户观看.针对直播平台的分析也随之成为流媒体服务领域的一个研究热点.直播过程中精彩片段的自动提取对于标签生成、视频分类和内容推荐等方面而言至关重要,然而现有的精彩片段检测大多围绕音频、视频数据本身展开,如视频语义分析、音频情感感知等,缺乏对用户交互属性的合理利用.本文以斗鱼直播平台为例,通过分析观众的发弹幕与送礼物行为,提出了基于直播间弹幕数量时间序列和礼物价值时间序列的精彩片段自动化检测方法.首先利用z-score方法检测序列高潮,然后对高潮做样本标注和特征构建,最后采用随机森林对序列高潮分类并识别内容高潮,即精彩片段.结果表明,模型能够以较高的准确率完成精彩片段的自动化识别任务.

Crowdsourced live video streaming,which attracts vast number of users by its rich viewer-broadcaster interaction mechanism,has flourished and expanded over the past few years.The analysis of live video streaming platform has become a research hotspot in the field of streaming media services.Automatic extraction of highlights in live video streaming is crucial for tag generation,video classification and content recommendation.However,the existing highlight detection analysis mostly focuses on audio or video data itself,such as video semantic analysis,audio emotional perception,etc.,lacking the rational use of user interaction attributes.In this study,we take Douyu live video streaming platform as a case study.Through analysis of the viewer’s danmu posting and virtual gift donating behavior,we propose an automatic content highlight detection method based on the time series of danmu quantity and virtual gift value in the broadcasting.Firstly,we use z-score method to detect the sequence highlights,then we conduct highlight sample labeling and feature constructing.Finally,Random Forest is used to classify sequence highlights and identify the content highlights.The results show that the model we proposed can accomplish the task of automatic content highlight detection with high accuracy.