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首页> 外文会议>International Conference on Automatic Face and Gesture Recognition >Lend me a Hand: Auxiliary Image Data Helps Interaction Detection
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Lend me a Hand: Auxiliary Image Data Helps Interaction Detection

机译：借给我一只手：辅助图像数据有助于交互检测

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摘要

In social settings, people interact in close proximity. When analyzing such encounters from video, we are typically interested in distinguishing between a large number of different interactions. Here, we address training deformable part models (DPMs) for the detection of such interactions from video, in both space and time. When we consider a large number of interaction classes, we face two challenges. First, we need to distinguish between interactions that are visually more similar. Second, it becomes more difficult to obtain sufficient specific training examples for each interaction class. In this paper, we address both challenges and focus on the latter. Specifically, we introduce a method to train body part detectors from nonspecific images with pose information. Such resources are widely available. We introduce a training scheme and an adapted DPM formulation to allow for the inclusion of this auxiliary data. We perform cross-dataset experiments to evaluate the generalization performance of our method. We demonstrate that our method can still achieve decent performance, from as few as five training examples.
机译：在社交环境中，人们互相互动。在从视频中分析此类遭遇时，我们通常对区分大量不同的相互作用感兴趣。这里，我们地址训练可变形部件模型（DPMS）以检测来自视频的这种交互，在空间和时间。当我们考虑大量的互动类时，我们面临两个挑战。首先，我们需要区分视觉上更相似的相互作用。其次，对每个交互类别获得足够的特定训练示例变得更加困难。在本文中，我们解决了两种挑战并专注于后者。具体地，我们介绍一种用姿势信息从非特异性图像训练身体部位探测器的方法。这些资源广泛可用。我们介绍训练方案和适应的DPM配方，以允许包含这种辅助数据。我们执行跨数据集实验，以评估我们方法的泛化性能。我们证明我们的方法仍然可以实现体面的性能，从少数五个训练例子。

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