操作模式发现:非参数贝叶斯方法

Bingbing Ni, P. Moulin
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引用次数: 2

摘要

我们的目标是无监督地发现人类在辅助生活等场景中操纵各种物体的行动(运动)模式。我们的动机来自两个关键观察。首先,与被操作的各种类型的对象相关的运动模式存在很大的变化,因此手动定义运动原语是不可行的。其次,一些运动模式在被操纵的不同对象之间共享,而另一些则是对象特定的。因此,我们提出了一种非参数贝叶斯方法,该方法在以无监督的方式学习具有代表性的操作(运动)模式之前采用分层狄利克雷过程。该概率模型以易于获取的目标检测得分图和密集的运动轨迹为输入,通过共享的操作模式字典发现与不同类型的被操作对象相关的运动模式组。学习字典的大小会自动推断出来。在两个辅助生活基准和一个烹饪动作数据集上的综合实验证明了我们学习的操作模式字典在表示操作动作以进行识别方面的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Manipulation Pattern Discovery: A Nonparametric Bayesian Approach
We aim to unsupervisedly discover human's action (motion) patterns of manipulating various objects in scenarios such as assisted living. We are motivated by two key observations. First, large variation exists in motion patterns associated with various types of objects being manipulated, thus manually defining motion primitives is infeasible. Second, some motion patterns are shared among different objects being manipulated while others are object specific. We therefore propose a nonparametric Bayesian method that adopts a hierarchical Dirichlet process prior to learn representative manipulation (motion) patterns in an unsupervised manner. Taking easy-to-obtain object detection score maps and dense motion trajectories as inputs, the proposed probabilistic model can discover motion pattern groups associated with different types of objects being manipulated with a shared manipulation pattern dictionary. The size of the learned dictionary is automatically inferred. Comprehensive experiments on two assisted living benchmarks and a cooking motion dataset demonstrate superiority of our learned manipulation pattern dictionary in representing manipulation actions for recognition.
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