基于GPU的高效视频拷贝检测技术的研究与实现

发布时间：2018-10-10 10:07

【摘要】：互联网中存在着大量内容重复的视频。如何对大规模视频进行高效的拷贝检测是长期以来一直受到研究者们关注的问题。高效的拷贝检测算法有非常广泛的应用,首先,视频拷贝检测可以用于计算视频的相似性。可以用于基于内容的视频检索。目前用户检索视频时,多是通过文本的方式检索视频的标题或作者信息。视频拷贝检测可以为用户提供基于内容的检索服务,即通过视频的内容来检索,方便用于检索出合适的视频资源。其次,视频拷贝检测可以去除视频集中的重复视频,可以提升搜索引擎搜索结果的质量。用户在检索视频时,总是希望搜索引擎返回的结果中重复内容越少越好,拷贝检测可以通过去重为用户提供更有价值的信息。再次,视频拷贝检测可以用来进行违法违规视频检测,复制检测,对于知识产权的保护也有非常重要的价值。最后,还有很多任务依赖于高效的视频拷贝检测,例如视频聚类等任务。这些任务的核心步骤之一,就是计算视频集中视频之间的相似度。基于视频间两两相似度进行后续处理。研究如何提升大规模视频集上的拷贝检测性能,对于这些应用而言意义重大。近年来随着视频集规模的逐渐增大,传统的串行视频拷贝检测算法已经不能处理日益增大的视频集,本文通过GPU并行计算框架来对于视频拷贝检测任务进行并行化处理。在并行环境下,进行大规模视频的拷贝检测,必须要首先考虑三个问题：一、如何提取适合并行计算的视频特征；二、如何设计并行拷贝检测的算法；三、如何针对GPU的硬件特殊性进行优化。以往的研究没有充分考虑这三个方面的问题,影响了算法的性能。本文着重研究了这三个问题,提出了一种基于GPU的高效视频拷贝检测算法。
[Abstract]:There are a lot of repetitive videos on the Internet. How to detect large-scale video copies efficiently is a problem that researchers have been paying close attention to for a long time. Efficient copy detection algorithm has a wide range of applications. Firstly, video copy detection can be used to calculate the similarity of video. Can be used for content-based video retrieval. At present, when users retrieve video, most of them retrieve video title or author information by text. Video copy detection can provide users with content-based retrieval services, that is, through video content retrieval, it is convenient to retrieve suitable video resources. Secondly, video copy detection can remove repetitive video from video set and improve the quality of search engine search results. When retrieving video, users always hope that the less duplicate content the search engine returns, the more valuable information can be provided by copy detection. Thirdly, video copy detection can be used for illegal video detection and copy detection, which is also very important for the protection of intellectual property rights. Finally, many tasks depend on efficient video copy detection, such as video clustering. One of the core steps of these tasks is to calculate the similarity between video sets. The subsequent processing is based on the similarity between two videos. It is very important for these applications to study how to improve the performance of copy detection on large scale video sets. In recent years, with the increasing of video set size, the traditional serial video copy detection algorithm can not deal with the growing video set. This paper uses the GPU parallel computing framework to parallelize the video copy detection task. In parallel environment, we must first consider three problems for large-scale video copy detection: first, how to extract video features suitable for parallel computing; second, how to design parallel copy detection algorithm; third, How to optimize the hardware particularity of GPU. Previous studies have not fully considered these three aspects, which affect the performance of the algorithm. This paper focuses on these three problems and proposes an efficient video copy detection algorithm based on GPU.
【学位授予单位】：复旦大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TP391.41

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