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A Trajectory Based Method of Automatic Counting of Cyclist in Traffic Video Data.

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Title: A Trajectory Based Method of Automatic Counting of Cyclist in Traffic Video Data.
Authors: Shahraki, Farideh Foroozandeh1, Yazdanpanah, Ali Pour1, Regentova, Emma E.1, Muthukumar, Venkatesan1
Source: International Journal on Artificial Intelligence Tools. Aug2017, Vol. 26 Issue 4, p-1. 20p.
Subjects: Cyclists, Digital counters, Videos, Acquisition of data, Problem solving, Accidents
Abstract: Due to the growing number of cyclist accidents on urban roads, methods for collecting information on cyclists are of significant importance to the Department of Transportation. The collected information provides insights into solving critical problems related to transportation planning, implementing safety countermeasures, and managing traffic flow efficiently. Intelligent Transportation System (ITS) employs automated tools to collect traffic information from traffic video data. One of the important factors that influence cyclists safety is their counts. In comparison to other road users, such as cars and pedestrians, the automated cyclist data collection is relatively a new research area. In this work, we develop a vision-based method for gathering cyclist count data at intersections and road segments. We implement a robust cyclist detection method based on a combination of classification features. We implement a multi-object tracking method based on the Kernelized Correlation Filters (KCF) in cooperation with the bipartite graph matching algorithm to track multiple cyclists. Then, a trajectory rebuilding method and a trajectory comparison model are applied to refine the accuracy of tracking and counting. The proposed method is the first cyclist counting method, that has the ability to count cyclists under different movement patterns. The trajectory data obtained can be further utilized for cyclist behavioral modeling and safety analysis. [ABSTRACT FROM AUTHOR]
Copyright of International Journal on Artificial Intelligence Tools is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: A Trajectory Based Method of Automatic Counting of Cyclist in Traffic Video Data.
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  Data: <searchLink fieldCode="DE" term="%22Cyclists%22">Cyclists</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+counters%22">Digital counters</searchLink><br /><searchLink fieldCode="DE" term="%22Videos%22">Videos</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink><br /><searchLink fieldCode="DE" term="%22Accidents%22">Accidents</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Due to the growing number of cyclist accidents on urban roads, methods for collecting information on cyclists are of significant importance to the Department of Transportation. The collected information provides insights into solving critical problems related to transportation planning, implementing safety countermeasures, and managing traffic flow efficiently. Intelligent Transportation System (ITS) employs automated tools to collect traffic information from traffic video data. One of the important factors that influence cyclists safety is their counts. In comparison to other road users, such as cars and pedestrians, the automated cyclist data collection is relatively a new research area. In this work, we develop a vision-based method for gathering cyclist count data at intersections and road segments. We implement a robust cyclist detection method based on a combination of classification features. We implement a multi-object tracking method based on the Kernelized Correlation Filters (KCF) in cooperation with the bipartite graph matching algorithm to track multiple cyclists. Then, a trajectory rebuilding method and a trajectory comparison model are applied to refine the accuracy of tracking and counting. The proposed method is the first cyclist counting method, that has the ability to count cyclists under different movement patterns. The trajectory data obtained can be further utilized for cyclist behavioral modeling and safety analysis. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of International Journal on Artificial Intelligence Tools is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1142/S0218213017500154
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      – Code: eng
        Text: English
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        PageCount: 20
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      – SubjectFull: Cyclists
        Type: general
      – SubjectFull: Digital counters
        Type: general
      – SubjectFull: Videos
        Type: general
      – SubjectFull: Acquisition of data
        Type: general
      – SubjectFull: Problem solving
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      – SubjectFull: Accidents
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      – TitleFull: A Trajectory Based Method of Automatic Counting of Cyclist in Traffic Video Data.
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            NameFull: Shahraki, Farideh Foroozandeh
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            NameFull: Yazdanpanah, Ali Pour
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            NameFull: Regentova, Emma E.
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            NameFull: Muthukumar, Venkatesan
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              M: 08
              Text: Aug2017
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              Y: 2017
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