IEEE Trans Pattern Anal Mach Intell. 2017 Aug;39(8):1489-1503. doi: 10.1109/TPAMI.2016.2608884. Epub 2016 Sep 13.
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.
轨迹分析在许多应用中至关重要。在本文中,我们解决了以高度信息丰富的方式表示运动轨迹的问题,并因此利用它来分析轨迹。我们的方法首先利用给定轨迹的完整信息来构建热传递场,该场提供了一种丰富的上下文方式来描述场景中的全局运动模式。然后,通过整合热传递场中包含的周围运动模式,导出一个 3D 管来描绘输入轨迹。3D 管有效地:1)保留轨迹的运动信息,2)嵌入轨迹周围的完整上下文运动模式,3)以清晰统一的方式可视化轨迹信息。我们进一步引入了基于水滴的过程。它从 3D 管中派生出一个水滴向量,从而以简单而有效的方式描述高维 3D 管信息。最后,我们将我们的管和水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。与最先进算法的实验比较证明了我们方法的有效性。