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基于2.5D图匹配的动作识别范例
Action Recognition with Exemplar Based 2.5D Graph Matching |
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| 课程网址: |
http://videolectures.net/eccv2012_yao_action/
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| 主讲教师: |
Ivan Laptev ; Michael J. Black; Bangpeng Yao
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| 开课单位: |
斯坦福大学
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| 开课时间: |
信息不详。欢迎您在右侧留言补充。
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| 课程语种: |
英语
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| 中文简介: |
本文研究了静止图像中人类行为的识别问题。我们做出了两项关键贡献。（1）我们提出了一种新颖的2.5d动作图像表示方法，既考虑了独立于视角的姿态信息，又考虑了丰富的外观信息。动作图像的2.5d图包括一组作为人体关键点的节点，以及一组作为节点之间空间关系的边。每个关键点由独立于视图的三维位置和局部二维外观特征表示。然后，通过匹配相应的2.5d图，可以测量两个动作图像之间的相似性。（2）我们使用一种基于示例的行为分类方法，其中为每个行为类选择一组具有代表性的图像。与其他类别相比，所选图像覆盖了较大的动作变化，并携带了识别信息。这种基于示例的行为类表示进一步使我们的方法能够健壮地构成变化和阻塞。我们在两个公开可用的数据集上测试了我们的方法，结果表明它实现了非常有希望的性能。
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| 课程简介: |
This paper deals with recognizing human actions in still images. We make two key contributions. (1) We propose a novel, 2.5D representation of action images that considers both viewindependent pose information and rich appearance information. A 2.5D graph of an action image consists of a set of nodes that are keypoints of the human body, as well as a set of edges that are spatial relationships between the nodes. Each key-point is represented by view-independent 3D positions and local 2D appearance features. The similarity between two action images can then be measured by matching their corresponding 2.5D graphs. (2) We use an exemplar based action classification approach, where a set of representative images are selected for each action class. The selected images cover large within-action variations and carry discriminative information compared with the other classes. This exemplar based representation of action classes further makes our approach robust to pose variations and occlusions. We test our method on two publicly available datasets and show that it achieves very promising performance.
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| 关 键 词: |
人类行动; 静止图像; 计算机视觉; 行动图像
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-12-10：cwx
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| 阅读次数: |
66
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