Journal of South China University of Technology(Natural Science) >

2019 , Vol. 47 >Issue 4: 10 - 19

DOI: https://doi.org/10.12141/j.issn.1000-565X.180186

Electronics, Communication & Automation Technology

Recognition of Passengers＇Abnormal Behavior on the Escalator Based on Human Skeleton Sequence

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  • 1． School of Automation Science and Engineering，South China University of Technology，Guangzhou 510640，Guangdong，China; 2． Key Laboratory of Autonomous Systems and Network Control of Ministry of Education，Guangzhou 510640，Guangdong，China; 3． Hitachi Elevator (Guangzhou) Escalator Limited Liability Company，Guangzhou 510660，Guangzhou，China
田联房(1969-)，男，博士，教授，主要从事模式识别与人工智能等研究．

Received date: 2018-04-23

  Online published: 2019-03-01

Supported by

Supported by the National Ministry of Science and Technology Hai Phong Public Welfare Project(201505002) and the Special Funds for Frontier and Key Technology Innovation of Guangdong(2016B090912001)

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Abstract

The research on recognition of passengers＇ abnormal behavior on the escalator is of great significance． The traditional behavior recognition algorithm can not accurately recognize the multi-target in real time，and the re- cognition result is easily affected by the environment change． So an algorithm for recognizing passengers＇abnormal behavior on the escalator based on human skeleton sequences was proposed． Firstly，the passenger＇s face was de- tected by the support vector machine and tracked by the improved kernelized correlation filter to obtain the trajecto- ries of the passengers． Then，the human skeleton sequences of passengers were extracted by the convolutional neu- ral network． After that，the abnormal behavior skeleton sequences were detected from the human skeleton se- quences of passengers through template matching． Finally，the abnormal behavior was recognized by matching its skeleton sequence with all kinds of abnormal behavior skeleton sequences through dynamic time warping． The re- sults of experiment on 10 escalator videos show that the algorithm achieves a processing speed of 10 frames per se- cond and the recognition accuracy rate is 93. 2%，so it can accurately recognize a variety of passenger＇s abnormal behaviors in real time．

Key words： escalator; human skeleton sequence; abnormal behavior recognition; support vector machines; ker- nelized correlation filter; convolutional neural network; dynamic time warping

Cite this article

TIAN Lianfang WU Qichao DU Qiliang HUANG Liguang LI Miao ZHANG Daming . Recognition of Passengers＇Abnormal Behavior on the Escalator Based on Human Skeleton Sequence[J]. Journal of South China University of Technology(Natural Science), 2019 , 47(4) : 10 -19 . DOI: 10.12141/j.issn.1000-565X.180186

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