Inferring home locations for users from spatiotemporal data has become increasingly important for real-world applications ranging from security, recommendation, advertisement targeting, to transportation scheduling. Existing home location inference studies are based either on geo-tagged social media data or continuous GPS data. Yet this inference problem in highly sparse vehicle trajectories in urban surveillance systems remains largely unexplored. In this paper, we propose an accurate home location inference framework for vehicles in urban traffic surveillance systems by considering both spatial and temporal characteristics. To the best of our knowledge, we are the first to predict exact home community for vehicles at such a fine granularity using the sparse and noisy surveillance camera data. First, we collect and preprocess multiple contextual datasets to obtain a context-rich road network with residential communities and surveillance cameras. Second, we detect the potential home location areas for each vehicle by clustering Origin-Destination ($O$-$D$) pairs extracted in vehicle’s camera-based trajectories. Then we further propose an $in∕out$ time pattern to distinguish the home area candidate from the $O$-$D$ clusters by leveraging time-aware constraints. Furthermore, to find the exact home community, we propose a Kernel Density Estimation (KDE) based inference method with a local camera selection strategy to effectively identify the home community from the residential communities near/in the home area candidate. Our comprehensive experiments on a large-scale real-world dataset demonstrate the effectiveness of our proposed method.

从时空数据推断用户的家位置对于从安全性，推荐性，广告定位到运输调度等实际应用而言，变得越来越重要。现有的家庭位置推断研究基于带有地理标签的社交媒体数据或连续的GPS数据。然而，在城市监视系统中高度稀疏的车辆轨迹中的这种推理问题仍未得到充分探索。在本文中，我们通过考虑空间和时间特征，为城市交通监控系统中的车辆提出了一种精确的家中位置推断框架。据我们所知，我们是第一个使用稀疏且嘈杂的监控摄像头数据以如此精细的粒度预测车辆的确切家庭社区的。第一，我们收集并预处理多个上下文数据集，以获得具有居民社区和监控摄像头的上下文丰富的道路网络。其次，我们通过聚类Origin-Destination（$Ø$--$d$）从车辆基于相机的轨迹中提取的对。然后，我们进一步提出$\mathrm{一世}ñ∕ØüŤ$ 区分家乡地区候选人和 $Ø$--$d$通过利用时间感知约束来集群。此外，为了找到确切的家庭社区，我们提出了一种基于核密度估计（KDE）的推理方法，该方法具有本地相机选择策略，可以从靠近或位于该家庭区域候选者的住宅社区中有效地识别家庭社区。我们在大规模的真实世界数据集上的综合实验证明了我们提出的方法的有效性。