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    Automated Human Use Mapping of Social Infrastructure by Deep Learning Methods Applied to Smart City Camera Systems

    Source: Journal of Computing in Civil Engineering:;2022:;Volume ( 036 ):;issue: 004::page 04022011
    Author:
    Peng Sun
    ,
    Gabriel Draughon
    ,
    Rui Hou
    ,
    Jerome P. Lynch
    DOI: 10.1061/(ASCE)CP.1943-5487.0000998
    Publisher: ASCE
    Abstract: With the emergence of the smart city, there is a growing need for scalable methods that sense how humans interact and use infrastructure in order to model social behaviors relevant to designing sustainable and resilient built environments. Cyber-physical system (CPS) frameworks used to monitor and automate infrastructure systems in smart cities can be extended to sense people to better understand how they use infrastructure systems including social infrastructure (e.g., parks, markets). This paper adopts convolutional neural network (CNN) architectures to automate the detection and spatiotemporal mapping of people using camera data to form a cyber-physical-social system (CPSS) for smart cities. The Mask region based convolutional neural network (R-CNN) detector was adopted and tailored to identify and segment human subjects in real time using camera images with an average speed of 7 frames per second. The Mask R-CNN framework was trained end to end using the Objects in Public Open Spaces (OPOS) image data set that includes classified segmentations of people in public spaces. A two-dimensional/three-dimensional (2D-3D) lifting algorithm based on a monocular camera calibration model was also employed to accurately position detected people in space. Finally, a Hungarian assignment algorithm based on association metrics extracted from detected people was used to assign people to spatiotemporal trajectories. To demonstrate the proposed framework, this study used the Detroit riverfront parks to study how people utilize community parks, which are a form of social infrastructure. The Mask R-CNN detector is proven precise in detecting and classifying the behavior of people in parks with mean average precision well above 85% for all class types defined in the OPOS library. The framework is also shown to be effective in spatially mapping the various uses of park furnishings, leading to better management of parks.
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      Automated Human Use Mapping of Social Infrastructure by Deep Learning Methods Applied to Smart City Camera Systems

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4283107
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    • Journal of Computing in Civil Engineering

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    contributor authorPeng Sun
    contributor authorGabriel Draughon
    contributor authorRui Hou
    contributor authorJerome P. Lynch
    date accessioned2022-05-07T20:56:59Z
    date available2022-05-07T20:56:59Z
    date issued2022-04-04
    identifier other(ASCE)CP.1943-5487.0000998.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4283107
    description abstractWith the emergence of the smart city, there is a growing need for scalable methods that sense how humans interact and use infrastructure in order to model social behaviors relevant to designing sustainable and resilient built environments. Cyber-physical system (CPS) frameworks used to monitor and automate infrastructure systems in smart cities can be extended to sense people to better understand how they use infrastructure systems including social infrastructure (e.g., parks, markets). This paper adopts convolutional neural network (CNN) architectures to automate the detection and spatiotemporal mapping of people using camera data to form a cyber-physical-social system (CPSS) for smart cities. The Mask region based convolutional neural network (R-CNN) detector was adopted and tailored to identify and segment human subjects in real time using camera images with an average speed of 7 frames per second. The Mask R-CNN framework was trained end to end using the Objects in Public Open Spaces (OPOS) image data set that includes classified segmentations of people in public spaces. A two-dimensional/three-dimensional (2D-3D) lifting algorithm based on a monocular camera calibration model was also employed to accurately position detected people in space. Finally, a Hungarian assignment algorithm based on association metrics extracted from detected people was used to assign people to spatiotemporal trajectories. To demonstrate the proposed framework, this study used the Detroit riverfront parks to study how people utilize community parks, which are a form of social infrastructure. The Mask R-CNN detector is proven precise in detecting and classifying the behavior of people in parks with mean average precision well above 85% for all class types defined in the OPOS library. The framework is also shown to be effective in spatially mapping the various uses of park furnishings, leading to better management of parks.
    publisherASCE
    titleAutomated Human Use Mapping of Social Infrastructure by Deep Learning Methods Applied to Smart City Camera Systems
    typeJournal Paper
    journal volume36
    journal issue4
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/(ASCE)CP.1943-5487.0000998
    journal fristpage04022011
    journal lastpage04022011-21
    page21
    treeJournal of Computing in Civil Engineering:;2022:;Volume ( 036 ):;issue: 004
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian