王培晓, 吴升, 张恒才, 陆锋, 王宏恩. 一种室内人群时空聚集区域识别方法[J]. 武汉大学学报 ( 信息科学版), 2021, 46(5): 790-798. DOI: 10.13203/j.whugis20190228
引用本文: 王培晓, 吴升, 张恒才, 陆锋, 王宏恩. 一种室内人群时空聚集区域识别方法[J]. 武汉大学学报 ( 信息科学版), 2021, 46(5): 790-798. DOI: 10.13203/j.whugis20190228
WANG Peixiao, WU Sheng, ZHANG Hengcai, LU Feng, WANG Hong'en. A Method for Identifying Spatial and Temporal Aggregation Area of Indoor Crowd[J]. Geomatics and Information Science of Wuhan University, 2021, 46(5): 790-798. DOI: 10.13203/j.whugis20190228
Citation: WANG Peixiao, WU Sheng, ZHANG Hengcai, LU Feng, WANG Hong'en. A Method for Identifying Spatial and Temporal Aggregation Area of Indoor Crowd[J]. Geomatics and Information Science of Wuhan University, 2021, 46(5): 790-798. DOI: 10.13203/j.whugis20190228

一种室内人群时空聚集区域识别方法

A Method for Identifying Spatial and Temporal Aggregation Area of Indoor Crowd

  • 摘要: 现有的人群聚集区域识别算法主要集中于室外空间,与室外空间相比,室内三维空间结构复杂,室内人群聚集更易导致安全事故的产生。提出了一种室内人群聚集区域识别方法——室内简化重构聚集方法(indoor simplification reconstruction cluster,IndoorSRC)。首先,设计了一种新型的室内时空凝聚层次聚类算法(indoor spatial-temporal agglomerative nesting, Indoor-STAGNES)识别室内用户停留点,简化室内用户移动轨迹;然后,构建了一种基于卡尔曼滤波的室内轨迹重构方法,实现了室内用户轨迹的对齐与重采样;最后,提出了一种室内时空密度聚类算法(indoor spatial-temporal ordering points to identify the clustering structure,Indoor-STOPTICS)发现室内三维时空人群聚集区域。采用真实室内轨迹数据进行实验分析,结果表明,与传统的室外识别方法相比,在运行时间差别不大的情况下,IndoorSRC识别的误识率可降低23.7%。

     

    Abstract:
      Objectives  Compared with the outdoor space, the indoor three-dimensional space structure is complex, and indoor crowd gathering is more likely to lead to safety accidents.As the development of indoor positioning technology, it is possible to collect indoor trajectory, which also provides an important data source for the identification of indoor crowd gathering area.
      Methods  We propose a novel method called IndoorSRC (indoor simplification reconstruction cluster) to detect indoor crowd gathering areas. Firstly, a new indoor user trajectory simplification algorithm, indoor spatial-temporal agglomerative nesting (Indoor-STAGNES), is designed to identify indoor user stay points and simplify indoor user's trajectory. Then, an indoor trajectory reconstruction method based on Kalman filter is constructed to realize the alignment and resampling of indoor user trajectories. Finally, a new indoor space-time density clustering algorithm: Indoor spatial-temporal ordering points to identify the clustering structure (Indoor-STOPTICS) is proposed to find indoor three-dimensional space-time crowd gathering area.
      Results  The real shopping mall indoor trajectory data are used for experimental analysis.The experimental results show that: (1)The gathering areas in the mall are mostly concentrated in the noon time and are mainly located in the dining area.(2) Compared with the traditional outdoor identification method, the error of recognition can be reduced by 23.7% in the case of IndoorSRC with little difference in running time.
      Conclusions  IndoorSRC can provide an effective supplement to the indoor crowd gathering area identification, and provide technical support for early warning and emergency rescue of indoor safety accidents.

     

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