Categorizing object-action relations from semantic scene graphs
Categorizing object-action relations from semantic scene graphs
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DOI：
10.1109/robot.2010.5509319
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发表时间：
2010-05
期刊：
影响因子：
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通讯作者：
E. Aksoy;A. Abramov;F. Wörgötter;B. Dellen
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文献类型：
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作者：
E. Aksoy;A. Abramov;F. Wörgötter;B. Dellen
在这项工作中，我们介绍了一种新的方法来检测时空对象的动作关系，导致双方，动作识别和对象分类。语义场景图从图像序列中提取，并用于通过精确的图形匹配技术找到动作序列的特征主图，从而提供动作场景的事件表，该事件表允许提取对象-动作关系。该方法适用于几个人工和真实的动作场景包含有限的上下文。这种方法的新奇在于它是无模型的，既不需要对象也不需要动作的先验表示。本质上，动作被识别而不需要先前的对象知识，并且对象仅基于它们在动作序列中所表现出的角色而被分类。因此，这种方法是基于启示原则，这在机器人技术中引起了很大的关注，并提供了一个前进的道路，通过反复实验的试错学习的对象-动作关系。因此，它可能是有用的识别和分类任务，例如在模仿学习的发展和认知机器人。
In this work we introduce a novel approach for detecting spatiotemporal object-action relations, leading to both, action recognition and object categorization. Semantic scene graphs are extracted from image sequences and used to find the characteristic main graphs of the action sequence via an exact graph-matching technique, thus providing an event table of the action scene, which allows extracting object-action relations. The method is applied to several artificial and real action scenes containing limited context. The central novelty of this approach is that it is model free and needs a priori representation neither for objects nor actions. Essentially actions are recognized without requiring prior object knowledge and objects are categorized solely based on their exhibited role within an action sequence. Thus, this approach is grounded in the affordance principle, which has recently attracted much attention in robotics and provides a way forward for trial and error learning of object-action relations through repeated experimentation. It may therefore be useful for recognition and categorization tasks for example in imitation learning in developmental and cognitive robotics.