Video Indexing, Search, Detection, and Description with Focus on TRECVID
Video Indexing, Search, Detection, and Description with Focus on TRECVID
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DOI：
10.1145/3078971.3079044
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发表时间：
2017-06
期刊：
影响因子：
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通讯作者：
G. Awad;Duy-Dinh Le;C. Ngo;Vinh-Tiep Nguyen;G. Quénot;Cees G. M. Snoek;S. Satoh
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文献类型：
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作者：
G. Awad;Duy-Dinh Le;C. Ngo;Vinh-Tiep Nguyen;G. Quénot;Cees G. M. Snoek;S. Satoh
过去十年，视频数据有了巨大的增长。人们比以往任何时候都更多地使用手机和平板电脑来拍摄、分享或观看视频。摄像机几乎遍布我们周围的公共领域（例如商店、街道、公共设施等）。不同的应用迫切需要高效且有效的检索方法。 TRECVID 的目标是通过提供大型测试集、统一的评分程序以及为有兴趣比较其结果的组织提供的论坛来鼓励基于内容的视频检索研究。在本教程中，我们介绍并讨论一些最重要和基本的基于内容的视频检索问题，例如识别预定义的视觉概念、在视频中搜索复杂的临时用户查询、通过视频数据集中的图像/视频示例搜索以检索特定的对象、人或位置、检测事件，最后通过研究系统如何自动用自然语言描述视频来弥合视觉和语言之间的差距。不同的常规 TRECVID 参与团队将回顾最新技术、当前挑战和未来方向，并提供有用资源的指导。每个团队将展示以下任务之一： 语义索引 (SIN) 零示例 (0Ex) 视频搜索 (AVS) 实例搜索 (INS) 多媒体事件检测 (MED) 视频转文本 (VTT)
There has been a tremendous growth in video data the last decade. People are using mobile phones and tablets to take, share or watch videos more than ever before. Video cameras are around us almost everywhere in the public domain (e.g. stores, streets, public facilities, ...etc). Efficient and effective retrieval methods are critically needed in different applications. The goal of TRECVID is to encourage research in content-based video retrieval by providing large test collections, uniform scoring procedures, and a forum for organizations interested in comparing their results. In this tutorial, we present and discuss some of the most important and fundamental content-based video retrieval problems such as recognizing predefined visual concepts, searching in videos for complex ad-hoc user queries, searching by image/video examples in a video dataset to retrieve specific objects, persons, or locations, detecting events, and finally bridging the gap between vision and language by looking into how can systems automatically describe videos in a natural language. A review of the state of the art, current challenges, and future directions along with pointers to useful resources will be presented by different regular TRECVID participating teams. Each team will present one of the following tasks: Semantic INdexing (SIN) Zero-example (0Ex) Video Search (AVS) Instance Search (INS) Multimedia Event Detection (MED) Video to Text (VTT)