9
41 1 Vol.41, No.1 2021 1 Systems Engineering — Theory & Practice Jan., 2021 doi: 10.12011/SETP2019-2960 : U491 : A , , , ( , 410073) , . , , , . , , . , , . , , . ; ; ; ; Pedestrian data mining with object tracking and trajectory clustering SAI Bin, CAO Ziqiang, TAN Yuejin, L ¨ U Xin (College of Systems Engineering, National University of Defense Technology, Changsha 410073, China) Abstract With the Internet of Things, big data, artificial intelligence making breakthroughs in the field of security, public video monitoring systems have developed quickly in recent years. The equipment generates massive amount of unstructured data, through analysis and research on pedestrian trajectory of video data, it can be found that the hidden behavior patterns contained which have an important research value. The article uses the multiple object tracking algorithm based on object detection to extract and describe the pedestrian movement trajectory in the surveillance video of subway station and mall exits, and then analyzed the trajectory pattern of pedestrians on the basis of trajectory. Aiming at the characteristics of pedestrian trajectory, a trajectory clustering method based on trajectory similarity was designed and implemented on the basis of point density clustering algorithm. The results showed that the method can effectively extract pedestrian trajectories, and extract trajectory patterns from large types of trajectory data. Keywords object detection; multiple object tracking; trajectory clustering; trajectory pattern; crowd behavior 1 , , , : 2019-12-31 : (1991–), , , , : ; (1996–), , , : ; (1958–), , , , , : ; : (1984–), , , , : . : (71771213, 91846301, 71790615, 71774168); (2017RS3040, 2018JJ1034) Foundation item: National Natural Science Foundation of China (71771213, 91846301, 71790615, 71774168); Science and Technology Program of Hunan (2017RS3040, 2018JJ1034) : , , , . [J]. , 2021, 41(1): 231–239. : Sai B, Cao Z Q, Tan Y J, et al. Pedestrian data mining with object tracking and trajectory clustering[J]. Systems Engineering — Theory & Practice, 2021, 41(1): 231–239.

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Page 1: Pedestrian data mining with object tracking and trajectory

� 41�� 1� ��������� Vol.41, No.1

2021 � 1 � Systems Engineering — Theory & Practice Jan., 2021

doi: 10.12011/SETP2019-2960 ������: U491 �����: A

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Pedestrian data mining with object tracking and trajectoryclustering

SAI Bin, CAO Ziqiang, TAN Yuejin, LU Xin

(College of Systems Engineering, National University of Defense Technology, Changsha 410073, China)

Abstract With the Internet of Things, big data, artificial intelligence making breakthroughs in the field of

security, public video monitoring systems have developed quickly in recent years. The equipment generates

massive amount of unstructured data, through analysis and research on pedestrian trajectory of video

data, it can be found that the hidden behavior patterns contained which have an important research value.

The article uses the multiple object tracking algorithm based on object detection to extract and describe

the pedestrian movement trajectory in the surveillance video of subway station and mall exits, and then

analyzed the trajectory pattern of pedestrians on the basis of trajectory. Aiming at the characteristics

of pedestrian trajectory, a trajectory clustering method based on trajectory similarity was designed and

implemented on the basis of point density clustering algorithm. The results showed that the method can

effectively extract pedestrian trajectories, and extract trajectory patterns from large types of trajectory

data.

Keywords object detection; multiple object tracking; trajectory clustering; trajectory pattern; crowd

behavior

1 AB

��������������, ����������, ����, �����

C�ÆD: 2019-12-31

EF��: (1991–), �, �, ��, �Æ�: �� �� �; ��� (1996–), �, ��, �Æ�: �� �� �; ��� (1958–), �, �, ���, , �Æ�: ���������������; ����: ��(1984–), �, �, , �Æ�: �� �����������.

��GH: �������� (71771213, 91846301, 71790615, 71774168); ��������� (2017RS3040, 2018JJ1034)Foundation item: National Natural Science Foundation of China (71771213, 91846301, 71790615, 71774168); Science and

Technology Program of Hunan (2017RS3040, 2018JJ1034)

IJKL�M: , ���, ���, �. ����������������� ��� [J]. ���������,

2021, 41(1): 231–239.

NJKL�M: Sai B, Cao Z Q, Tan Y J, et al. Pedestrian data mining with object tracking and trajectory clustering[J].

Systems Engineering — Theory & Practice, 2021, 41(1): 231–239.

Page 2: Pedestrian data mining with object tracking and trajectory

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Page 6: Pedestrian data mining with object tracking and trajectory

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