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Predicting Pedestrian Counts for Crossing Scenario Based on Fused Infrared-Visual Videos
Joint Authors
Dong, Decun
Yu, Rongjie
Huang, Shize
Chen, Wei
Yang, Xiaolu
Source
Journal of Advanced Transportation
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-04
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Estimating the number of pedestrians based upon surveillance videos and images has been a critical task while implementing intelligent signal controls at intersections.
However, this has been a difficult task considering the pedestrian waiting area is an outdoor scenario with complex and time-varying surrounding environment.
In this study, a method for estimating pedestrian counts based on multisource video data has been proposed.
First, the partial least squares regression (PLSR) model is developed to estimate the number of pedestrians from single-source video (either visible light video or infrared video).
Meanwhile, the temporal feature of the scenario (daytime or nighttime) is identified based on visible light video.
According to the recognized time periods, pedestrian count detection results from the visible light and infrared video data can be obtained with preset corresponding confidence levels.
The empirical experiments showed that this fusion method based on environment perception holds the benefits of 24-hour monitoring for outdoor scenarios at the pedestrian waiting area and substantially improved accuracy of pedestrian counting.
American Psychological Association (APA)
Huang, Shize& Chen, Wei& Yu, Rongjie& Yang, Xiaolu& Dong, Decun. 2018. Predicting Pedestrian Counts for Crossing Scenario Based on Fused Infrared-Visual Videos. Journal of Advanced Transportation،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1181787
Modern Language Association (MLA)
Huang, Shize…[et al.]. Predicting Pedestrian Counts for Crossing Scenario Based on Fused Infrared-Visual Videos. Journal of Advanced Transportation No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1181787
American Medical Association (AMA)
Huang, Shize& Chen, Wei& Yu, Rongjie& Yang, Xiaolu& Dong, Decun. Predicting Pedestrian Counts for Crossing Scenario Based on Fused Infrared-Visual Videos. Journal of Advanced Transportation. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1181787
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1181787