基于Tube-and-Droplet的运动轨迹表示与分析方法
A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories
摘要：
轨迹分析在许多应用中是必不可少的。在本文中，我们解决的问题，表示运动轨迹在一个高度信息化的方式，从而利用它来分析轨迹。我们的方法首先利用完整的信息从给定的轨迹来构建一个热传递场，它提供了一个上下文丰富的方式来描述在场景中的全局运动模式。然后，一个3D管的推导，它描绘了一个输入轨迹，通过整合其周围的运动模式中包含的热传递场。3D tube有效地：1）保持轨迹的运动信息，2）围绕轨迹嵌入完整的上下文运动模式，3）以清晰和统一的方式可视化关于轨迹的信息。我们进一步引入基于液滴的过程。该方法从三维管中提取液滴矢量，以简单有效的方式表征三维管的高维信息。最后，我们将我们的管和液滴表示应用于轨迹分析应用，包括轨迹聚类，轨迹分类和异常检测，以及3D动作识别。与最先进的算法的实验比较证明了我们的方法的有效性。
英文摘要：
Trajectory analysis is essential in many applications. In this paper, we address the problem of representing motion trajectories in a highly informative way, and consequently utilize it for analyzing trajectories. Our approach first leverages the complete information from given trajectories to construct a thermal transfer field which provides a context-rich way to describe the global motion pattern in a scene. Then, a 3D tube is derived which depicts an input trajectory by integrating its surrounding motion patterns contained in the thermal transfer field. The 3D tube effectively: 1) maintains the movement information of a trajectory, 2) embeds the complete contextual motion pattern around a trajectory, 3) visualizes information about a trajectory in a clear and unified way. We further introduce a droplet-based process. It derives a droplet vector from a 3D tube, so as to characterize the high-dimensional 3D tube information in a simple but effective way. Finally, we apply our tube-and-droplet representation to trajectory analysis applications including trajectory clustering, trajectory classification & abnormality detection, and 3D action recognition. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
