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Cinematography sequences tracking by means of fingerprinting techniques

机译:通过指纹技术跟踪摄影序列

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Video fingerprints are short features extracted from a video sequence in order to uniquely identify its visual content and its replicas. By advancing a new robust fingerprinting method, the present paper takes the challenge of designing an enabler for the use of Internet as a distribution tool in cinematography. In this respect, a 2D-DWT-based robust video fingerprinting method is designed so as to address two use cases, namely the retrieval of video content from a database and the tracking of in-theater camcorder recorded video content. A set of largest absolute value wavelet coefficients is considered as the fingerprint and a repeated statistical test is used as the matching procedure. The video dataset consists of two corpora, one for each use case. The first corpus regroups 3 h of heterogeneous original content (organized under the framework of the HD3D-IIO French national project) and of its attacked versions (a total of 21 h of video content). The second corpus consists of 3 h of heterogeneous content (i.e., HD3D-IIO corpus) and of 1 h of live camcorder recorded video content (a total of 4 h of video content). The inner 2D-DWT properties with respect to content-preserving attacks (such as linear filtering, sharpening, geometric, conversion to grayscale, small rotations, contrast changes, brightness changes, and live camcorder recording) ensure the following results: in the first use case, the probability of false alarm and missed detection are lower than 0.0005, precision and recall are higher than 0.97; in the second use case, the probability of false alarm is 0.00009, the probability of missed detection is lower than 0.0036, precision and recall are equal to 0.72.
机译:视频指纹是从视频序列中提取的简短特征,目的是唯一标识其视觉内容及其副本。通过提出一种新的鲁棒的指纹识别方法,本论文面临设计一个使能器,以将互联网用作摄影中的分发工具的挑战。在这方面,设计了一种基于2D-DWT的鲁棒性视频指纹识别方法,以解决两个用例,即从数据库中检索视频内容和跟踪便携式摄像机中录制的视频内容。将一组最大绝对值小波系数视为指纹,并将重复的统计检验用作匹配过程。视频数据集包含两个语料库,每个用例一个。第一个语料库将3小时的异类原始内容(在HD3D-IIO法国国家项目的框架下组织)及其受攻击的版本重新组合(总共21小时的视频内容)。第二个语料库由3小时的异构内容(即HD3D-IIO语料库)和1小时的实时摄像机录制的视频内容(总共4小时的视频内容)组成。与内容保留攻击有关的内部2D-DWT属性(例如线性过滤,锐化,几何,转换为灰度,小旋转,对比度变化,亮度变化和实时摄像机录制)可确保获得以下结果:首次使用误报,漏检的概率小于0.0005,精度和召回率大于0.97。在第二个用例中,错误警报的概率为0.00009,漏检的概率小于0.0036,精度和召回率等于0.72。

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