Keyframe extraction for human motion capture data based on affinity propagation
Keyframe extraction for human motion capture data based on affinity propagation
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DOI：
10.1109/iemcon.2018.8614862
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
Bin Sun;Dehui Kong;Shaofan Wang;Jinghua Li
中科院分区：
文献类型：
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作者：
Bin Sun;Dehui Kong;Shaofan Wang;Jinghua Li
关键帧提取是视频检索的重要内容。为了实现频率自适应的人体运动序列重采样，获得高质量的关键帧，提出了一种新的人体运动序列关键帧提取方法。首先，根据人体各部位的特征，定义了帧间相似性度量。然后，利用仿射传播聚类算法实现关键帧的提取。该方法从视频本身的信息分布出发，自适应地搜索视频的最优关键帧，运算速度快。最后，对基于关键帧的序列重构算法进行了验证。在CMU数据库上进行的对比实验验证了该方法的有效性。
Keyframe extraction is important for video retrieval. In order to realize frequency adaptive human motion sequence resampling and achieve high quality keyframe, we propose a new keyframe extraction method for human motion sequence. First, we define the inter-frame similarity metric based on the features of human body parts. Then, the keyframe extraction is realized by the affine propagation clustering algorithm. The proposed method starts from the information distribution of the video itself, adaptively searches for the optimal keyframe of the video, and the operation speed is fast. Finally, the evaluation of the sequence reconstruction based on keyframe is verified. A comparative experiment conducted on the CMU database verified the efficiency of our method.