大型商场中移动群组识别与位置预测方法
|
摘要
根据大型商场中人员密度大且流动性强的特点,对室内场所中的动态群组进行识别和位置预测,提出移动对象位置和方向特征相结合的空间-时序聚类群组识别方法。在群组位置预测中,考虑数据集的增量更新给出序列树的存储结构,只需扫描一次数据库即可得到频繁区域序列以及对应的关联规则,同时能够进行单步和多步的位置预测。给出结合群组出现时间和人数的位置预测方法,提高群组位置预测的准确度。在ATC数据集进行实验,结果表明,当群组对象检测率达到87.6%时,该方法群组识别准确度可达到90.3%,与LAR、TLAR等算法相比,单步和多步位置预测准确度分别达到91.2%和33.8%。
According to the characteristics of large density and strong mobility in large shopping malls,in order to identify and predict the dynamic groups in indoor spaces,a method is proposed to identify the groups by spatial-sequence clustering combined with the moving object position and direction features.In the group location prediction,the sequential tree storage structure is proposed in consideration of the incremental updating of the dataset.This structure can obtain the frequent area sequences and the corresponding association rules by scanning the database only once,and can perform single-step and multi-step position predictions.In order to improve the accuracy of group location prediction,a method based on group appearance time and group number is proposed.Experimental verification is carried out in the ATC data set,and results show that when the detection rate of group objects reaches 87.6% by using this method,the accuracy of group identification reaches 90.3%,compared with the algorithms such as LAR and TLAR,the single-step and multi-step position prediction accuracy reach 91.2% and 33.8% respectively.
According to the characteristics of large density and strong mobility in large shopping malls,in order to identify and predict the dynamic groups in indoor spaces,a method is proposed to identify the groups by spatial-sequence clustering combined with the moving object position and direction features.In the group location prediction,the sequential tree storage structure is proposed in consideration of the incremental updating of the dataset.This structure can obtain the frequent area sequences and the corresponding association rules by scanning the database only once,and can perform single-step and multi-step position predictions.In order to improve the accuracy of group location prediction,a method based on group appearance time and group number is proposed.Experimental verification is carried out in the ATC data set,and results show that when the detection rate of group objects reaches 87.6% by using this method,the accuracy of group identification reaches 90.3%,compared with the algorithms such as LAR and TLAR,the single-step and multi-step position prediction accuracy reach 91.2% and 33.8% respectively.
引文
[1] MOUSSAID M,PEROZO N,GAMIER S,et al.The walking behaviour of pedestrian social groups and its impact on crowd dynamics[J].PloS One,2010,5(4):10047.
[2] ROGGEN D,WURZ M,TROSTER G,et al.Recognition of crowd behavior from mobile sensors with pattern analysis and graph clustering methods[J].Networks and Heterogeneous Media,2011,6(3):521-544.
[3] WIRZ M,SCHLAPFER P,KJERGAARD M B,et al.Towards an online detection of pedestrian flocks in urban canyons by smoothed spatio-temporal clustering of GPS trajectories[C]//Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-based Social Networks.New York,USA:ACM Press,2011:17-24.
[4] DU H,YU Z,YI F,et al.Group mobility classification and structure recognition using mobile devices[C]//Proceedings of 2016 IEEE International Conference on Pervasive Computing and Communications.Washington D.C.,USA:IEEE Press,2016:1-9.
[5] FEESE S,AMRICH B,TROSTER G,et al.Sensing group proximity dynamics of firefighting teams using smartphones[C]//Proceedings of the 2013 International Symposium on Wearable Computers.New York,USA:ACM Press,2013:97-104.
[6] ZHU W,CAO J,CHAN H C B,et al.Mobile RFID with a high identification rate[J].IEEE Transactions on Computers,2014,63(7):1778-1792.
[7] 武帆,谭小彬,申晶晶,等.基于天线覆盖模型的RFID室内定位算法[J].计算机工程,2017,43(3):120-125.
[8] 李岩.固定场景中运动目标检测与运动估计跟踪[D].郑州:郑州大学,2016.
[9] 王妍,邓庆绪,刘赓浩,等.结合运动方程与卡尔曼滤波的动态目标追踪预测算法[J].计算机科学,2015,42(12):76-81.
[10] 乔少杰,李天瑞,韩楠,等.大数据环境下移动对象自适应轨迹预测模型[J].软件学报,2015,26(11):2869-2883.
[11] CHO S B.Exploiting machine learning techniques for location recognition and prediction with smartphone logs[J].Neurocomputing,2016,176:98-106.
[12] IMIELIENSKIN T,SWAMI A,AGRAWAL R.Mining association rules between set of items in largedatabases[J].ACM SIGMOD Record,1993,22(2):207-216.
[13] WIRZ M,SCHLAPFER P,KJERGAARD M B,et al.Towards an online detection of pedestrian flocks in urban canyons by smoothed spatio-temporal clustering of GPS trajectories[C]//Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-based Social Networks.New York,USA:ACM Press,2011:17-24.
[14] KALNIS P,MAMOULIS N,BAKIRAS S.On discovering moving clusters in spatio-temporal data[C]//Proceedings of SSTD’05.Washington D.C.,USA:IEEE Press,2005:364-381.
[15] ZHOU C,FRANKOWSKI D,LUDFORD P,et al.Discovering personal gazetteers:an interactive clustering approach[C]//Proceedings of the 12th Annual ACM International Workshop on Geographic Information Systems.New York,USA:ACM Press,2004:266-273.
[16] JACCARD P.The distribution of the flora in the alpine zone[J].New Phytologist,1912,11(2):37-50.
[17] KRISTINA P.基于位置的社交网络中的POI推荐问题的研究[D].哈尔滨:哈尔滨工业大学,2016.
[18] GAO H,TANG J,HU X,et al.Content-aware point of interest recommendation on location-based social networks[C]//Proceedings of AAAI’15.Washington D.C.,USA:IEEE Press,2015:1721-1727.
[19] BRSCIC D,KANDA T,IKEDA T,et al.Person tracking in large public spaces using 3-D range sensors[J].IEEE Transactions on Human-Machine Systems,2013,43(6):522-534.
[2] ROGGEN D,WURZ M,TROSTER G,et al.Recognition of crowd behavior from mobile sensors with pattern analysis and graph clustering methods[J].Networks and Heterogeneous Media,2011,6(3):521-544.
[3] WIRZ M,SCHLAPFER P,KJERGAARD M B,et al.Towards an online detection of pedestrian flocks in urban canyons by smoothed spatio-temporal clustering of GPS trajectories[C]//Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-based Social Networks.New York,USA:ACM Press,2011:17-24.
[4] DU H,YU Z,YI F,et al.Group mobility classification and structure recognition using mobile devices[C]//Proceedings of 2016 IEEE International Conference on Pervasive Computing and Communications.Washington D.C.,USA:IEEE Press,2016:1-9.
[5] FEESE S,AMRICH B,TROSTER G,et al.Sensing group proximity dynamics of firefighting teams using smartphones[C]//Proceedings of the 2013 International Symposium on Wearable Computers.New York,USA:ACM Press,2013:97-104.
[6] ZHU W,CAO J,CHAN H C B,et al.Mobile RFID with a high identification rate[J].IEEE Transactions on Computers,2014,63(7):1778-1792.
[7] 武帆,谭小彬,申晶晶,等.基于天线覆盖模型的RFID室内定位算法[J].计算机工程,2017,43(3):120-125.
[8] 李岩.固定场景中运动目标检测与运动估计跟踪[D].郑州:郑州大学,2016.
[9] 王妍,邓庆绪,刘赓浩,等.结合运动方程与卡尔曼滤波的动态目标追踪预测算法[J].计算机科学,2015,42(12):76-81.
[10] 乔少杰,李天瑞,韩楠,等.大数据环境下移动对象自适应轨迹预测模型[J].软件学报,2015,26(11):2869-2883.
[11] CHO S B.Exploiting machine learning techniques for location recognition and prediction with smartphone logs[J].Neurocomputing,2016,176:98-106.
[12] IMIELIENSKIN T,SWAMI A,AGRAWAL R.Mining association rules between set of items in largedatabases[J].ACM SIGMOD Record,1993,22(2):207-216.
[13] WIRZ M,SCHLAPFER P,KJERGAARD M B,et al.Towards an online detection of pedestrian flocks in urban canyons by smoothed spatio-temporal clustering of GPS trajectories[C]//Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-based Social Networks.New York,USA:ACM Press,2011:17-24.
[14] KALNIS P,MAMOULIS N,BAKIRAS S.On discovering moving clusters in spatio-temporal data[C]//Proceedings of SSTD’05.Washington D.C.,USA:IEEE Press,2005:364-381.
[15] ZHOU C,FRANKOWSKI D,LUDFORD P,et al.Discovering personal gazetteers:an interactive clustering approach[C]//Proceedings of the 12th Annual ACM International Workshop on Geographic Information Systems.New York,USA:ACM Press,2004:266-273.
[16] JACCARD P.The distribution of the flora in the alpine zone[J].New Phytologist,1912,11(2):37-50.
[17] KRISTINA P.基于位置的社交网络中的POI推荐问题的研究[D].哈尔滨:哈尔滨工业大学,2016.
[18] GAO H,TANG J,HU X,et al.Content-aware point of interest recommendation on location-based social networks[C]//Proceedings of AAAI’15.Washington D.C.,USA:IEEE Press,2015:1721-1727.
[19] BRSCIC D,KANDA T,IKEDA T,et al.Person tracking in large public spaces using 3-D range sensors[J].IEEE Transactions on Human-Machine Systems,2013,43(6):522-534.