图 书 馆
运动目标的周期行为挖掘Mining Periodic Behaviors for Moving Objects |
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| 课程网址: | http://videolectures.net/kdd2010_li_mpbm/ |
| 主讲教师: | Zhenhui Jessie Li |
| 开课单位: | 宾夕法尼亚州立大学 |
| 开课时间: | 2014-10-01 |
| 课程语种: | 英语 |
| 中文简介: |  周期性是移动物体经常发生的现象。寻找周期性行为对于理解物体运动至关重要。然而，周期性行为可能很复杂，涉及多个交错周期、部分时间跨度以及时空噪声和异常值。在本文中，我们解决了挖掘移动对象的周期性行为的问题。它涉及两个子问题：如何检测复杂运动中的周期，以及如何挖掘周期运动行为。我们的主要假设是观察到的运动是由与某些参考位置相关的多个交错的周期性行为产生的。基于这个假设，我们提出了一个两阶段算法，Periodica，来解决这个问题。在第一阶段，提出观察点的概念来捕获参考位置。通过观察点，可以使用结合傅立叶变换和自相关的方法来检索运动中的多个周期。在第二阶段，提出了一个概率模型来表征周期性行为。对于特定时期，周期性行为是通过层次聚类从局部运动序列统计概括的。对合成数据集和真实数据集的实证研究证明了我们方法的有效性。 |
| 课程简介: | Periodicity is a frequently happening phenomenon for moving objects. Finding periodic behaviors is essential to understanding object movements. However, periodic behaviors could be complicated, involving multiple interleaving periods, partial time span, and spatiotemporal noises and outliers. In this paper, we address the problem of mining periodic behaviors for moving objects. It involves two sub-problems: how to detect the periods in complex movement, and how to mine periodic movement behaviors. Our main assumption is that the observed movement is generated from multiple interleaved periodic behaviors associated with certain reference locations. Based on this assumption, we propose a two-stage algorithm, Periodica, to solve the problem. At the first stage, the notion of observation spot is proposed to capture the reference locations. Through observation spots, multiple periods in the movement can be retrieved using a method that combines Fourier transform and autocorrelation. At the second stage, a probabilistic model is proposed to characterize the periodic behaviors. For a specific period, periodic behaviors are statistically generalized from partial movement sequences through hierarchical clustering. Empirical studies on both synthetic and real data sets demonstrate the effectiveness of our method. |
| 关 键 词: | 复杂运动周期; 两阶段算法; 合成数据集 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-06-09：zyk |
| 最后编审: | 2021-06-09：zyk |
| 阅读次数: | 52 |