Structural Approach for Event Resolution in Cricket Videos

S. Premaratne, K. Jayaratne
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引用次数: 6

Abstract

Classification of multimedia big data today has become a serious issue for organizations. Therefore, new concepts to mine of multimedia big have emerged and people are doing numerous researches on how to effectively handle different types of multimedia data also known as multi model data. In our research, we focus on how to effectively extract and classify data from a multimedia data related to sports videos and draw conclusions considering all media present in the content. We specifically considered the game of cricket in this research to build a multi-model mining approach to identify specific events. We consider low level details such as color variations in videos and pitch in audio to analyze and distinguish different attributes of a given event. The input can be any cricket video and our approach is to identify events such as a four, a six, a dot ball etc. by extracting low level details of its color variations, edges, camera changes and audio frequency variations. This research for video/audio data extraction provides room for addition of further audio data extraction and textual data extraction for classification of a multimodal data set.
板球视频事件分辨率的结构方法
多媒体大数据的分类已经成为当今组织面临的一个严重问题。因此,多媒体数据挖掘的新概念应运而生,人们对如何有效地处理不同类型的多媒体数据也就是多模型数据进行了大量的研究。在我们的研究中,我们的重点是如何有效地从与体育视频相关的多媒体数据中提取和分类数据,并得出考虑内容中所有媒体的结论。在本研究中,我们特别考虑了板球比赛,建立了一个多模型挖掘方法来识别特定事件。我们考虑低层次的细节,如视频中的颜色变化和音频中的音调,以分析和区分给定事件的不同属性。输入可以是任何板球视频,我们的方法是通过提取其颜色变化、边缘、摄像机变化和音频变化的低层次细节来识别事件,如4、6、点球等。本文对视频/音频数据提取的研究为进一步对多模态数据集进行音频数据提取和文本数据提取的分类提供了空间。
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