Based On Subspace Analysis Angle Of View Video Technology Research

With the rapid development of digital multimedia technology,network communication technology and digital television technology,video data emerged in the form of blowout in every day.At the same time,with the popularity of explosive monitoring and the explosion of sports and entertainment in recent years,multi view videos have gradually become a main type of video family.We get the multi view video data in the same period of time from a plurality of cameras in different locations with different perspectives on the same events.Its non-structured data form result in a low efficiency when processing or browsing,and cannot meet the needs of the development of practical application.At present,it’s a question of the researchers need to be addressed that how to analyze,manage,storage and transmit the massive multi view video data efficiently.Video summarization can analyze the content and structure of video data through the way of automatic or semi-automatic,and extract the typical video segments in the video data.Reasonable application of video summarization,can remove the redundancy of video data effectively,and then improve the efficiency of video retrieval and browsing.The solution of the problem of the storage,transmission and management of multi view video,is of great significance to public security,public transport,public entertainment,sports and other fields.This paper using the experience of single video summarization,analyzing the result features of multi view video and combining linear space assumption,meaning that the same scenario goals in multiple points of view existed in the same linear space,which is widely-used in the application of cross view video,proposed multi view video abstracting algorithm based on subspace analysis.This paper in-depth study from three aspects,include video preprocessing based on subspace analysis,hyperspace building of cross view features based on the subspace mapping and multi view video abstracting,the concrete research content is as follows:(1)The research of multi view video preprocessing.At first,we analysis the structural information of multi view video,research on video segmentation method based on scene change detection,and segment the multi view video into video segmentation for different scenarios.Then we extract foreground image of frames from video segment of different scenarios,with the video prospect segmentation method updating based on sparse low rank subspace.At the last,we extract SIFT and optical-flow features,and fuse SIFT features and optical-flow feature as the underlying characteristics of video information.(2)In this paper,we structure base spaces for each view.At first obtain the projection matrix of each video segment of different scenarios through eliciting their features,and build the base space of each video segment of different scenarios.Then analyze the relationship between the space elements of each scenario subspace,and build across view features of each element in hyperspace through subspace projection to each other.(3)In this paper,We do research on video content clustering method based on sparse subspace under the cross view hyperspace.Cluster the characteristics hyperspace cross view we build,and select the appropriate video segments in each class as the representative video segments of scenario.Then generate multi view video summarization according to the time information of each video segment.Experiments on both public multi view video datasets show that,the multi view video summarization method based on subspace analysis proposed by this paper can effectively choose representative video segments from multi view video.The video summarization generated by this method contains the key events information of original video,and the video length become only a quarter of the original video length simultaneously.This method proposed by this paper provide a novel theoretical method for multi view video summarization,and also an effective technical means for the development and utilization of multi view video.