Efficiently Finding Individuals from Video Dataset
Efficiently Finding Individuals from Video Dataset
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DOI：
10.1587/transinf.e95.d.1280
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发表时间：
2012-05
期刊：
影响因子：
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通讯作者：
Pengyi Hao;S. Kamata
中科院分区：
文献类型：
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作者：
Pengyi Hao;S. Kamata
我们感兴趣的是从视频数据集中检索视频镜头或包含特定人物的视频。由于姿态、光照条件、遮挡、发型和面部表情的变化很大，人脸轨迹近年来在人脸识别、人脸检索和视频命名等领域得到了广泛的研究。然而，当人脸轨迹数量很大时，传统的人脸轨迹匹配方法在人脸轨迹中匹配全部或部分对人脸的效果不佳。因此，本文提出了一种从视频数据集中查找给定人物的高效方法。在我们的研究中，除了对单个视频中的人脸轨迹进行研究外，我们还考虑了如何在一个数据集中组织视频中的所有人脸，以及如何在查询过程中提高搜索质量。不同的视频可能包含同一个人；因此，对不同视频中的个体进行管理将有助于它们的检索。提出的方法包括以下三点。(1)首先基于具有时间约束的场景信息将每个视频中出现一段时间的同一人的人脸轨迹连接起来，然后采用提出的分层聚类方法对同一视频中的所有人进行组织。（ii）在获得一个视频中所有人的组织结构后，通过ffi传播将这些人组织成一个上层。（iii）最后，在查询过程中，采用基于视频索引结构的重测方法，提高检索精度。我们还建立了一个视频数据集，其中包含六种类型的视频：电影、电视节目、教育视频、采访、新闻发布会和国内活动。首先研究了6类视频中人脸轨迹的形成，然后在包含100多万张人脸和218786条人脸轨迹的视频数据集上进行了实验。结果表明，该方法具有较高的搜索质量和较短的搜索时间。
SUMMARY We are interested in retrieving video shots or videos containing particular people from a video dataset. Owing to the large variations in pose, illumination conditions, occlusions, hairstyles and facial expressions, face tracks have recently been researched in the fields of face recognition, face retrieval and name labeling from videos. However, when the number of face tracks is very large, conventional methods, which match all or some pairs of faces in face tracks, will not be e ff ective. Therefore, in this paper, an e ffi cient method for finding a given person from a video dataset is presented. In our study, in according to performing research on face tracks in a single video, we also consider how to organize all the faces in videos in a dataset and how to improve the search quality in the query process. Di ff erent videos may include the same person; thus, the management of individuals in di ff erent videos will be useful for their retrieval. The proposed method includes the following three points. (i) Face tracks of the same person appearing for a period in each video are first connected on the basis of scene information with a time constriction, then all the people in one video are organized by a proposed hierarchical clustering method. (ii) After obtaining the organizational structure of all the people in one video, the people are organized into an upper layer by a ffi nity propagation. (iii) Finally, in the process of querying, a remeasuring method based on the index structure of videos is performed to improve the retrieval accuracy. We also build a video dataset that contains six types of videos: films, TV shows, educational videos, interviews, press conferences and domestic activities. The formation of face tracks in the six types of videos is first researched, then experiments are performed on this video dataset containing more than 1 million faces and 218,786 face tracks. The results show that the proposed approach has high search quality and a short search time.