Co-movement pattern mining from GPS trajectories has been an intriguing subject in spatial-temporal data mining. In this paper, we extend this research line by migrating the data source from GPS sensors to surveillance cameras, and presenting the first investigation into co-movement pattern mining from videos. We formulate the new problem, re-define the spatial-temporal proximity constraints from cameras deployed in a road network, and theoretically prove its hardness. Due to the lack of readily applicable solutions, we adapt existing techniques and propose two competitive baselines using Apriori-based enumerator and CMC algorithm, respectively. As the principal technical contributions, we introduce a novel index called temporal-cluster suffix tree (TCS-tree), which performs two-level temporal clustering within each camera and constructs a suffix tree from the resulting clusters. Moreover, we present a sequence-ahead pruning framework based on TCS-tree, which allows for the simultaneous leverage of all pattern constraints to filter candidate paths. Finally, to reduce verification cost on the candidate paths, we propose a sliding-window based co-movement pattern enumeration strategy and a hashing-based dominance eliminator, both of which are effective in avoiding redundant operations. We conduct extensive experiments for scalability and effectiveness analysis. Our results validate the efficiency of the proposed index and mining algorithm, which runs remarkably faster than the two baseline methods. Additionally, we construct a video database with 1169 cameras and perform an end-to-end pipeline analysis to study the performance gap between GPS-driven and video-driven methods. Our results demonstrate that the derived patterns from the video-driven approach are similar to those derived from groundtruth trajectories, providing evidence of its effectiveness.

翻译：从GPS轨迹中挖掘协同移动模式一直是时空数据挖掘领域引人入胜的课题。本文通过将数据源从GPS传感器迁移至监控摄像机，拓展了该研究方向，并首次提出从视频中进行协同移动模式挖掘的研究。我们定义了新问题，重新定义了道路网络中部署摄像机的时空邻近约束条件，并在理论上证明了其计算复杂性。由于缺乏现成可用的解决方案，我们改编现有技术，分别基于Apriori枚举器和CMC算法提出了两个具有竞争力的基线方法。作为主要技术贡献，我们引入了一种名为时态簇后缀树（TCS-tree）的新型索引结构，该结构在每个摄像机内执行两级时态聚类，并从生成的聚类中构建后缀树。此外，我们提出了一种基于TCS-tree的序列前向剪枝框架，该框架能够同时利用所有模式约束来过滤候选路径。最后，为降低候选路径的验证代价，我们提出了基于滑动窗口的协同移动模式枚举策略和基于哈希的支配消除器，两者均能有效避免冗余操作。我们进行了大量实验以分析可扩展性和有效性。实验结果表明，所提出的索引和挖掘算法具有高效性，其运行速度显著快于两种基线方法。此外，我们构建了包含1169台摄像机的视频数据库，并进行了端到端管道分析，以研究GPS驱动方法与视频驱动方法之间的性能差距。结果表明，视频驱动方法导出的模式与地面真实轨迹导出的模式相似，验证了其有效性。