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中一个引人入胜的研究课题。本文通过将数据源从GPS传感器迁移至监控摄像头，拓展了这一研究方向，并首次开展了视频中协同移动模式挖掘的探索。我们提出了新问题的形式化定义，基于道路网络中部署的摄像头重新定义了时空邻近性约束，并从理论上证明了该问题的计算复杂性。由于缺乏现成的解决方案，我们改进了现有技术，并提出了两种具有竞争力的基线方法：基于Apriori的枚举器和CMC算法。作为主要技术贡献，我们引入了一种名为时间聚类后缀树（TCS-tree）的新型索引结构，该结构在每个摄像头内执行两级时间聚类，并基于所得聚类构建后缀树。此外，我们提出了一种基于TCS-tree的序列前瞻剪枝框架，可同时利用所有模式约束来筛选候选路径。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扩展性与有效性。实验结果验证了所提索引与挖掘算法的效率，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个摄像头的视频数据库，并完成了端到端流水线分析，以研究GPS驱动方法与视频驱动方法之间的性能差异。结果表明，视频驱动方法导出的模式与真实轨迹导出的模式高度相似，从而验证了该方法的有效性。