In action recognition, the dynamic image (DI) approach is recently proposed to code a video signal to a still image. Since DI descriptor is strongly dependent on first frames, it cannot extract dynamics that do not occur in the first frames or even long dynamics. On the other hand, most of the video frames are not informative for the task of action recognition. Therefore, the authors' intuition is that the process of representing a video using all frames is inefficient. Thus, in this study, they proposed to remove the existing redundancy inside the frames and extract some processed informative images based on the information theory which are called key frames. The proposed method is capable enough to extract sufficient frames regardless of the duration and the position of frames in the entire video. Motivated by this method and DI, they proposed a novel key frames dynamic image (KFDI) approach. Experimental results on popular UCF11, Olympic Sports, and J-HMDB datasets show the superiority of the proposed KFDI approach compared to the DI in capturing long dynamics of videos for action recognition. Their experiments show KFDI improves the accuracy 2–6% compared to DI.

中文翻译：

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使用改进的动态图像进行人类活动识别

在动作识别中，最近提出了动态图像（DI）方法，将视频信号编码为静止图像。由于DI描述符强烈依赖于第一帧，因此它无法提取出在第一帧中甚至长时间动态中都不会出现的动态。另一方面，大多数视频帧无法提供动作识别任务的信息。因此，作者的直觉是，使用所有帧表示视频的过程效率低下。因此，在这项研究中，他们提出了消除帧内部现有的冗余，并根据信息论提取一些经过处理的信息图像，称为关键帧。所提出的方法足以提取足够的帧，而与整个视频中帧的持续时间和位置无关。受这种方法和DI的激励，他们提出了一种新颖的关键帧动态图像（KFDI）方法。在流行的UCF11，Olympic Sports和J-HMDB数据集上的实验结果表明，与DI相比，所提出的KFDI方法在捕获视频的长动态性以进行动作识别方面具有优势。他们的实验表明，KFDI将精度提高了2–6％ 与DI相比。