ABSTRACT Detecting movement patterns with complicated spatial or temporal characteristics is a challenge. The past decade has witnessed the success of deep learning in processing image, voice and text data. However, its application in movement pattern detection is not fully exploited. To address the research gap, this paper develops a deep learning approach to detect regional dominant movement patterns (RDMP) in trajectory data. Specifically, a novel feature descriptor called directional flow image (DFI) is firstly proposed to store the local directional movement information in trajectory data. A DFI classification model called TRNet is designed based on convolutional neural network. The model is then trained with a synthetic trajectory dataset covering 11 classes of commonly encountered movement patterns in reality. Finally, a sliding window detector is built to detect RDMP at multiple scales and a clustering-based merging method is proposed to prune the redundant detection results. Training of TRNet on the synthetic dataset achieves considerably high accuracy. Experiments on a real-world taxi trajectory dataset further demonstrate the effectiveness and efficiency of the proposed approach in discovering complex movement patterns in trajectory data.

中文翻译：

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用卷积神经网络检测轨迹数据中的区域主导运动模式

摘要检测具有复杂空间或时间特征的运动模式是一项挑战。过去十年见证了深度学习在处理图像、语音和文本数据方面的成功。然而，它在运动模式检测中的应用还没有得到充分利用。为了解决研究空白，本文开发了一种深度学习方法来检测轨迹数据中的区域主导运动模式（RDMP）。具体而言，首先提出了一种称为定向流图像（DFI）的新型特征描述符，用于将局部定向运动信息存储在轨迹数据中。基于卷积神经网络设计了一个名为 TRNet 的 DFI 分类模型。然后使用合成轨迹数据集对模型进行训练，该数据集涵盖了现实中常见的 11 类运动模式。最后，构建了一个滑动窗口检测器来检测多尺度的RDMP，并提出了一种基于聚类的合并方法来修剪冗余检测结果。在合成数据集上训练 TRNet 实现了相当高的准确度。在真实世界出租车轨迹数据集上的实验进一步证明了所提出的方法在发现轨迹数据中复杂运动模式方面的有效性和效率。