Pedestrian data mining with object tracking and trajectory clustering

SAI Bin, CAO Ziqiang, TAN Yuejin, Lü Xin

Systems Engineering - Theory & Practice ›› 2021, Vol. 41 ›› Issue (1) : 231-239.

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Systems Engineering - Theory & Practice ›› 2021, Vol. 41 ›› Issue (1) : 231-239. DOI: 10.12011/SETP2019-2960

Pedestrian data mining with object tracking and trajectory clustering

  • SAI Bin, CAO Ziqiang, TAN Yuejin, Lü Xin
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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.

Key words

object detection / multiple object tracking / trajectory clustering / trajectory pattern / crowd behavior

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SAI Bin , CAO Ziqiang , TAN Yuejin , Lü Xin. Pedestrian data mining with object tracking and trajectory clustering. Systems Engineering - Theory & Practice, 2021, 41(1): 231-239 https://doi.org/10.12011/SETP2019-2960

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Funding

National Natural Science Foundation of China (71771213, 91846301, 71790615, 71774168); Science and Technology Program of Hunan (2017RS3040, 2018JJ1034)
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