基于语义分析方法的视频流媒体大数据技术研究
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摘要
视频语义分析是对视频信息所包含内容的描述和逻辑表示,涉及众多信息处理研究领域。大数据技术在数据存储、运算等方面具有优势,而粒计算理论在数据描述、特征分类等方面具有优势,因此将大数据技术和粒计算理论应用于视频语义分析系统具有明显的应用前景。
本论文针对流媒体大数据处理分析中面临的关键问题展开工作,对流媒体大数据存储模型、视频语义分析模型和视频分析关键算法进行了深入研究。首先讨论了视频数据处理的关键问题,针对视频语义分析的存储、查询问题提出了一种新的流媒体大数据存储架构,构建了视频数据按时间特征存储、检索的解决方案；其次,将大数据技术用于视频语义分析,并将粒计算理论用于视频数据的结构描述,建立了不同粒度下的分层视频语义分析模型；最后,针对视频处理运动物体检测和阴影抑制,提出了一种基于运动矢量检测的阴影抑制算法,并讨论了该算法在大数据系统的实现问题。
具体研究内容和创新点有如下几个方面：
1、设计了一种按时间特征寻址、检索和分析流媒体大数据的新型存储架构。该架构支持流媒体数据编解码压缩,可以按帧存储寻址,有利于在海量存储介质中实现快速定位和分析结果统一存储。
2、在流媒体大数据按帧存储寻址的基本结构上,提出了元数据描述的语义分析框架并建立了多层次数据库接口。设计了通用的流媒体大数据存储检索数据库,在此基础上可以根据不同应用建立各自的视频分析元数据模型以及数据库接口,解决了众多异构的视频采集模块、视频分析模块和系统的连接问题。
3、采用粒计算理论,将视频数据中图像特征、图像对象、视频对象、语义对象等物理结构映射为粒子,形成包含粒属性结构的视频流媒体粒度模型。将不同粒层的视频粒子采用不同的分析手段进行处理,可以简化视频分析的算法结构,同时也有利于采用大数据技术实现并行运算。
4、提出了基于运动矢量检测的运动物体阴影抑制算法,算法将视频编码信息中的运动矢量信息用于运动物体阴影抑制,取得了较好的抑制效果,最后采用视频流媒体粒度模型对算法进行建模设计。
Video semantic analysis is the description and logical representation of the contents in video information, which is a complicated process of information processing in many research areas. BigData technology has the advantage of data storage and data computing, and granular computing theory is good at data representation and feature classification. Therefore, both BigData technology and granular computing theory are combined and applied to video semantic analysis system, which can solve the key problems such as data storage, computing, and representation.
It is focused on structuring the storage framework for the big data in the streaming media, building the analysis model of video semantic, and proposing the key algorithms for video analysis. Firstly, the key problems in video data processing are discussed and a novel big data storage framework for streaming media is proposed to solve the storage and inquiry of the video semantic analysis results. The design solution is to storage and retrieves video data by time features. Secondly, the BigData technique is employed video semantic analysis and the granular computing theory is applied to the structure description of video data. On the basis, a model for the hierarchical video semantic analysis is built under the different granularities. At last, the detection for the moving object and shadow in video were discussed, and the motion vector detection-based shadow suppression algorithm was proposed and implemented in the big data systems.
The main research contents and innovative works in the dissertation include:
1. Structuring a novel storage framework for the addressing, retrieval and analysis of big data in the streaming media by time features. This framework supports the compression of encoding and decoding of streaming media data. It can store and address by frames and can realize the quick positioning and unified storage of analysis results in the mass storage medium.
2. Structuring the metadata description framework of semantic analysis and setting the interface for hierarchical database. A multipurpose database for the big data storage and retrieval in streaming media is designed to build their respective metadata model and database interfaces for different applications. It can solve the connection problems in the heterogeneous video capture modules and subsystems.
3. Mapping image features involving image objects, video objects and semantic objects into granules and building the granular model for video streaming media containing granular property structure. The video granule in different granular layer can be treated. It simplifies the algorithm structure of video analysis and good for the realization for parallel computing by adopting the Bigdata technique.
4. Proposing the motion vector detection-based shadow suppression algorithm. The motion vector is extracted from the video coding information and applied in the shadow suppression of the moving objects. The algorithm used video streaming media granular model for video background modeling which obtains a good result of shadow detection.
本论文针对流媒体大数据处理分析中面临的关键问题展开工作,对流媒体大数据存储模型、视频语义分析模型和视频分析关键算法进行了深入研究。首先讨论了视频数据处理的关键问题,针对视频语义分析的存储、查询问题提出了一种新的流媒体大数据存储架构,构建了视频数据按时间特征存储、检索的解决方案；其次,将大数据技术用于视频语义分析,并将粒计算理论用于视频数据的结构描述,建立了不同粒度下的分层视频语义分析模型；最后,针对视频处理运动物体检测和阴影抑制,提出了一种基于运动矢量检测的阴影抑制算法,并讨论了该算法在大数据系统的实现问题。
具体研究内容和创新点有如下几个方面：
1、设计了一种按时间特征寻址、检索和分析流媒体大数据的新型存储架构。该架构支持流媒体数据编解码压缩,可以按帧存储寻址,有利于在海量存储介质中实现快速定位和分析结果统一存储。
2、在流媒体大数据按帧存储寻址的基本结构上,提出了元数据描述的语义分析框架并建立了多层次数据库接口。设计了通用的流媒体大数据存储检索数据库,在此基础上可以根据不同应用建立各自的视频分析元数据模型以及数据库接口,解决了众多异构的视频采集模块、视频分析模块和系统的连接问题。
3、采用粒计算理论,将视频数据中图像特征、图像对象、视频对象、语义对象等物理结构映射为粒子,形成包含粒属性结构的视频流媒体粒度模型。将不同粒层的视频粒子采用不同的分析手段进行处理,可以简化视频分析的算法结构,同时也有利于采用大数据技术实现并行运算。
4、提出了基于运动矢量检测的运动物体阴影抑制算法,算法将视频编码信息中的运动矢量信息用于运动物体阴影抑制,取得了较好的抑制效果,最后采用视频流媒体粒度模型对算法进行建模设计。
Video semantic analysis is the description and logical representation of the contents in video information, which is a complicated process of information processing in many research areas. BigData technology has the advantage of data storage and data computing, and granular computing theory is good at data representation and feature classification. Therefore, both BigData technology and granular computing theory are combined and applied to video semantic analysis system, which can solve the key problems such as data storage, computing, and representation.
It is focused on structuring the storage framework for the big data in the streaming media, building the analysis model of video semantic, and proposing the key algorithms for video analysis. Firstly, the key problems in video data processing are discussed and a novel big data storage framework for streaming media is proposed to solve the storage and inquiry of the video semantic analysis results. The design solution is to storage and retrieves video data by time features. Secondly, the BigData technique is employed video semantic analysis and the granular computing theory is applied to the structure description of video data. On the basis, a model for the hierarchical video semantic analysis is built under the different granularities. At last, the detection for the moving object and shadow in video were discussed, and the motion vector detection-based shadow suppression algorithm was proposed and implemented in the big data systems.
The main research contents and innovative works in the dissertation include:
1. Structuring a novel storage framework for the addressing, retrieval and analysis of big data in the streaming media by time features. This framework supports the compression of encoding and decoding of streaming media data. It can store and address by frames and can realize the quick positioning and unified storage of analysis results in the mass storage medium.
2. Structuring the metadata description framework of semantic analysis and setting the interface for hierarchical database. A multipurpose database for the big data storage and retrieval in streaming media is designed to build their respective metadata model and database interfaces for different applications. It can solve the connection problems in the heterogeneous video capture modules and subsystems.
3. Mapping image features involving image objects, video objects and semantic objects into granules and building the granular model for video streaming media containing granular property structure. The video granule in different granular layer can be treated. It simplifies the algorithm structure of video analysis and good for the realization for parallel computing by adopting the Bigdata technique.
4. Proposing the motion vector detection-based shadow suppression algorithm. The motion vector is extracted from the video coding information and applied in the shadow suppression of the moving objects. The algorithm used video streaming media granular model for video background modeling which obtains a good result of shadow detection.
引文
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