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Efficient Activity Detection in Untrimmed Video with Max-Subgraph Search

机译：带有最大子图搜索的未修剪视频中的有效活动检测

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摘要

We propose an efficient approach for activity detection in video that unifies activity categorization with space-time localization. The main idea is to pose activity detection as a maximum-weight connected subgraph problem. Offline, we learn a binary classifier for an activity category using positive video exemplars that are “trimmed” in time to the activity of interest. Then, given a novel untrimmed video sequence, we decompose it into a 3D array of space-time nodes, which are weighted based on the extent to which their component features support the learned activity model. To perform detection, we then directly localize instances of the activity by solving for the maximum-weight connected subgraph in the test video's space-time graph. We show that this detection strategy permits an efficient branch-and-cut solution for the best-scoring-and possibly non-cubically shaped-portion of the video for a given activity classifier. The upshot is a fast method that can search a broader space of space-time region candidates than was previously practical, which we find often leads to more accurate detection. We demonstrate the proposed algorithm on four datasets, and we show its speed and accuracy advantages over multiple existing search strategies.
机译：我们提出了一种有效的视频活动检测方法，该方法将活动分类与时空本地化统一在一起。主要思想是将活动检测作为最大权重的连接子图问题。在离线状态下，我们使用积极的视频样本学习活动类别的二进制分类器，这些样本会及时“修剪”到感兴趣的活动。然后，给定一个新颖的未修剪视频序列，我们将其分解为一个时空节点的3D数组，并根据其组件特征支持学习的活动模型的程度对其进行加权。为了进行检测，我们然后通过在测试视频的时空图中求解最大权重连接的子图来直接定位活动实例。我们表明，对于给定的活动分类器，这种检测策略可以为视频的最佳评分和可能非立方体形状的部分提供有效的分支剪切解决方案。结果是一种快速的方法，可以比以前的实际方法搜索更大的时空候选区域空间，我们发现这通常会导致更准确的检测。我们在四个数据集上演示了该算法，并展示了其在多种现有搜索策略上的速度和准确性优势。

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