Action Recognition Using Discriminative Structured Trajectory Groups
Action Recognition Using Discriminative Structured Trajectory Groups
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DOI：
10.1109/wacv.2015.124
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发表时间：
2015-01
期刊：
影响因子：
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通讯作者：
I. Atmosukarto;N. Ahuja;Bernard Ghanem
中科院分区：
文献类型：
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作者：
I. Atmosukarto;N. Ahuja;Bernard Ghanem
在本文中，我们开发了一个新的框架，在视频中的动作识别。该框架是基于自动学习的歧视性的轨迹组相关的行动。与以前的方法不同，我们的方法不需要复杂的计算图匹配或复杂的潜在模型本地化的部分。我们将视频建模为具有潜在类变量的轨迹组的结构化袋。我们模型的动作识别问题在弱监督设置和学习歧视轨迹组采用多实例学习（MIL）基于支持向量机（SVM）使用预先计算的内核。内核依赖于提取的轨迹组和它们的相关特征之间的时空关系。我们证明了定量和定性的分类性能，我们提出的方法是上级的基线和几个国家的最先进的方法在三个具有挑战性的标准基准数据集。
In this paper, we develop a novel framework for action recognition in videos. The framework is based on automatically learning the discriminative trajectory groups that are relevant to an action. Different from previous approaches, our method does not require complex computation for graph matching or complex latent models to localize the parts. We model a video as a structured bag of trajectory groups with latent class variables. We model action recognition problem in a weakly supervised setting and learn discriminative trajectory groups by employing multiple instance learning (MIL) based Support Vector Machine (SVM) using pre-computed kernels. The kernels depend on the spatio-temporal relationship between the extracted trajectory groups and their associated features. We demonstrate both quantitatively and qualitatively that the classification performance of our proposed method is superior to baselines and several state-of-the-art approaches on three challenging standard benchmark datasets.