As indoor space is the primary place for pedestrian activities, obtaining intelligent monitoring of indoor pedestrians is crucial for intelligent video surveillance. Previous studies have verified the effectiveness of spatiotemporal constraints in multitarget multicamera tracking (MTMCT). Pedestrians are generally subjected to fine spatiotemporal constraints within buildings, based on which the indoor geographic information system (GIS) technology can obtain automatic spatiotemporal modeling. Combined with artificial intelligence (AI) technology, we established a research framework of “GIS+AI+IMPMCT. ” Specifically, we proposed indoor multipedestrian multicamera tracking (IMPMCT) based on fine spatiotemporal constraints. First, we used GIS to map the indoor monitoring images of buildings and automatically model the fine spatiotemporal relationship among the semantics of the entrance of the surveillance zone. Subsequently, we used the machine learning model of pedestrian localization and tracking to obtain local trajectories of pedestrians and combined them with map information to extract entrance semantics of trajectories. Finally, we used the local trajectory semantics and surveillance entrance semantic constraints to obtain a fine spatiotemporal constraint weight matrix between trajectories and fused pedestrian’s apparent features to obtain trajectory matching results. To verify our method, we established an IMPMCT data set containing fine indoor spatiotemporal information. Our method obtained an IDF1 of 0.805, which is better than those of other methods. Furthermore, the tracking results obtained by the proposed method contained both image space and geospatial trajectories.

中文翻译：

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基于精细时空约束的室内多人多摄像机跟踪


由于室内空间是行人活动的主要场所，因此获得室内行人的智能监控对于智能视频监控至关重要。先前的研究已经验证了时空约束在多目标多摄像机跟踪（MTMCT）中的有效性。行人在建筑物内普遍受到精细的时空约束，基于室内地理信息系统（GIS）技术可以实现自动时空建模。结合人工智能（AI）技术，建立了“GIS+AI+IMPMCT”的研究框架。具体来说，我们提出了基于精细时空约束的室内多人多摄像头跟踪（IMPMCT）。首先，我们利用GIS对建筑物的室内监控图像进行映射，并自动建模监控区域入口语义之间的精细时空关系。随后，我们利用行人定位与跟踪的机器学习模型来获取行人的局部轨迹，并将其与地图信息相结合，提取轨迹的入口语义。最后，利用局部轨迹语义和监控入口语义约束，得到轨迹之间精细的时空约束权重矩阵，融合行人表观特征，得到轨迹匹配结果。为了验证我们的方法，我们建立了包含精细室内时空信息的 IMPMCT 数据集。我们的方法获得了 0.805 的 IDF1，优于其他方法。此外，该方法获得的跟踪结果包含图像空间和地理空间轨迹。