LAUSR

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.