Abstract Online action detection aims to detect a current action from an untrimmed, streaming video, where only current and past frames are available. Recent methods for online action detection have focused on how to model discriminative representations from temporally partial information. However, they overlook the fact that the input video contains background as well as actions. To overcome this problem, in this paper, we propose a novel approach, named Temporal Filtering Network, to distinguish between relevant and irrelevant information from a partially observed, untrimmed video. Specifically, we present a filtering module to learn relevance scores indicating how relevant the information is to a current action. Our filtering module emphasizes the relevant information to a current action, while it filters out the information of background and unrelated actions. We conduct extensive experiments on THUMOS-14 and TVSeries datasets. On these datasets, the proposed method outperforms state-of-the-art methods by a large margin. We also show the effectiveness of the filtering module through comprehensive ablation studies.

中文翻译：

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用于在线动作检测的时间过滤网络

摘要 在线动作检测旨在从未修剪的流视频中检测当前动作，其中只有当前和过去的帧可用。最近的在线动作检测方法集中在如何从时间部分信息中对判别性表示进行建模。然而，他们忽略了输入视频包含背景和动作的事实。为了克服这个问题，在本文中，我们提出了一种名为时间过滤网络的新方法，用于区分部分观察到的未修剪视频中的相关信息和不相关信息。具体来说，我们提出了一个过滤模块来学习相关性分数，表明信息与当前动作的相关性。我们的过滤模块强调当前动作的相关信息，同时过滤掉背景和无关动作的信息。我们对 THUMOS-14 和 TVSeries 数据集进行了大量实验。在这些数据集上，所提出的方法大大优于最先进的方法。我们还通过综合消融研究展示了过滤模块的有效性。