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Spatial-temporal motion information integration for action detection and recognition in non-static background

机译：非静态背景中动作检测与识别的空间运动信息集成

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Various motion detection methods have been proposed in the past decade, but there are seldom attempts to investigate the advantages and disadvantages of different detection mechanisms so that they can complement each other to achieve a better performance. Toward such a demand, this paper proposes a human action detection and recognition framework to bridge the semantic gap between low-level pixel intensity change and the high-level understanding of the meaning of an action. To achieve a robust estimation of the region of action with the complexities of an uncontrolled background, we propose the combination of the optical flow field and Harris3D corner detector to obtain a new spatial-temporal estimation in the video sequences. The action detection method, considering the integrated motion information, works well with the dynamic background and camera motion, and demonstrates the advantage of the proposed method of integrating multiple spatial-temporal cues. Then the local features (SIFT and STIP) extracted from the estimated region of action are used to learn the Universal Background Model (UBM) for the action recognition task. The experimental results on KTH and UCF YouTube Action (UCF11) data sets show that the proposed action detection and recognition framework can not only better estimate the region of action but also achieve better recognition accuracy comparing with the peer work.
机译：在过去十年中提出了各种运动检测方法，但很少试图研究不同检测机制的优缺点，使得它们可以相互补充以实现更好的性能。本文提出了一种人体行动检测和识别框架来弥合低级像素强度变化与动作含义的高级了解之间的语义差距。为了实现与不受控制的背景的复杂性的动作区域的稳健估计，我们提出了光学流场和HARRIS3D拐角检测器的组合，以在视频序列中获得新的空间时间估计。考虑到集成运动信息的动作检测方法适用于动态背景和相机运动，并展示了集成多个空间时间线的提出方法的优点。然后，从估计的动作区域中提取的本地特征（SIFT和STIP）用于学习动作识别任务的通用背景模型（UBM）。 Kth和UCF YouTube动作（UCF11）数据集的实验结果表明，所提出的动作检测和识别框架不仅可以更好地估计动作区域，而且还可以实现与对等工作相比的更好的识别准确性。

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