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通过可转换字典对进行跨视图动作识别
Cross-View Action Recognition via a Transferable Dictionary Pair |
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| 课程网址: |
http://videolectures.net/bmvc2012_zheng_transferable_dictionary/
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| 主讲教师: |
Jingjing Zheng
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| 开课单位: |
马里兰大学
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| 开课时间: |
2012-10-09
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| 课程语种: |
英语
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| 中文简介: |
识别外观特征对于在固定的视点上识别动作是有效的，但是对于视点的变化很难一概而论。提出了一种基于稀疏表示的基于可转移字典对的视不变性动作识别方法。可转移字典对由两个字典组成，分别对应于源视图和目标视图。这两个字典是同时从不同视点拍摄的视频对中学习的，目的是鼓励这对视频中的每个视频具有相同的稀疏表示。因此，可转移字典对将两个视图之间对动作识别有用的特性链接起来。给出了学习可转移字典对的无监督算法和有监督算法。利用稀疏表示作为特征，将源视图中构建的分类器直接传输到目标视图中。我们扩展了我们的方法，将从多个源视图学到的操作模型转移到一个目标视图。我们证明了我们的方法在多视图IXMAS数据集上的有效性。我们的结果与目前的技术水平相比是有利的。
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| 课程简介: |
Discriminative appearance features are effective for recognizing actions in a fixed view, but generalize poorly to changes in viewpoint. We present a method for viewinvariant action recognition based on sparse representations using a transferable dictionary pair. A transferable dictionary pair consists of two dictionaries that correspond to the source and target views respectively. The two dictionaries are learned simultaneously from pairs of videos taken at different views and aim to encourage each video in the pair to have the same sparse representation. Thus, the transferable dictionary pair links features between the two views that are useful for action recognition. Both unsupervised and supervised algorithms are presented for learning transferable dictionary pairs. Using the sparse representation as features, a classifier built in the source view can be directly transferred to the target view. We extend our approach to transferring an action model learned from multiple source views to one target view. We demonstrate the effectiveness of our approach on the multi-view IXMAS data set. Our results compare favorably to the the state of the art.
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| 关 键 词: |
固定视图; 可转移字典; 目标视图
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| 课程来源: |
视频讲座网
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| 最后编审: |
2021-02-16：nkq
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| 阅读次数: |
46
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