Mining patterns of large-scale trajectory data streams has been of increase research interest. In this paper, we are interested in mining group patterns of moving objects. Group pattern mining describes a special type of trajectory mining task that requires to efficiently discover trajectories of objects that are found in close proximity to each other for a period of time. In particular, we focus on trajectories of pedestrians coming from motion video analysis and we are interested in interactive analysis and exploration of group dynamics, including various definitions of group gathering and dispersion. Traditional approaches to solve the problem adhere to strict definition of group semantics. That restricts their application to specific problems and renders them inadequate for many real-world scenarios. To address this limitation, we propose a novel versatile method, timeWgroups, for efficient discovery of pedestrian groups that can adhere to different pattern semantics. First, the method efficiently discovers pairs of pedestrians that move together over time, under varying conditions of space and time. Subsequently, pairs of pedestrians are used as a building block for effectively discovering groups of pedestrians that can satisfy versatile group pattern semantics. As such, the proposed method can accommodate many different scenarios and application requirements. In addition, we introduce a new group pattern, individual perspective grouping that focuses on how individuals perceive groups. Based on the new group pattern we define the concept of dominant groups, a global metric for defining important groups that respects the individual perspective group pattern. Through experiments on real data, we demonstrate the effectiveness of our methods on discovering group patterns of pedestrian trajectories against sensible baselines, for a varying range of conditions. Furthermore, a query-based search method is provided that allows for interactive exploration and analysis of group dynamics over time and space. In addition, a visual testing is performed on real motion video to assert the group dynamics discovered by our methods.

中文翻译：

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行人轨迹群模式挖掘的通用计算框架

大规模轨迹数据流的挖掘模式已引起越来越多的研究兴趣。在本文中，我们对挖掘运动对象的组模式感兴趣。组模式挖掘描述了一种特殊类型的轨迹挖掘任务，该任务需要有效地发现在一段时间内彼此非常接近的对象的轨迹。特别是，我们专注于来自运动视频分析的行人的轨迹，并且我们对交互式的分析和对群体动力学的探索感兴趣，包括对群体聚集和分散的各种定义。解决问题的传统方法坚持严格定义组语义。这限制了它们在特定问题上的应用，并使其不足以用于许多实际场景。为了解决此限制，我们提出了一种新颖的通用方法timeWgroups，用于有效发现可以遵循不同模式语义的行人组。首先，该方法有效地发现的行人对那个移动一起随时间，空间和时间变化的条件下。随后，将成对的行人用作构建块，以有效地发现行人组可以满足通用的组模式语义。这样，所提出的方法可以适应许多不同的场景和应用需求。此外，我们介绍了一种新的小组模式，即个人观点小组，重点关注个人如何看待小组。在新的群体模式的基础上，我们定义了优势群体的概念，这是一种定义重要群体的全球度量标准，该群体尊重个体视角的群体模式。通过对真实数据的实验，我们证明了我们的方法在各种条件下针对合理的基线发现行人轨迹的群体模式的有效性。此外，提供了基于查询的搜索方法，该方法允许互动探索和分析随时间和空间变化的群体动力学。另外，对真实运动视频进行视觉测试，以断言通过我们的方法发现的组动态。