Back to Search
Start Over
Predicting Pedestrian Counts for Crossing Scenario Based on Fused Infrared-Visual Videos
- Source :
- Journal of Advanced Transportation, Vol 2018 (2018)
- Publication Year :
- 2018
- Publisher :
- Hindawi Limited, 2018.
-
Abstract
- 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.
- Subjects :
- Economics and Econometrics
Article Subject
Infrared
Computer science
Strategy and Management
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
02 engineering and technology
Pedestrian
Signal
Task (project management)
0502 economics and business
Partial least squares regression
0202 electrical engineering, electronic engineering, information engineering
Computer vision
050210 logistics & transportation
Scenario based
business.industry
Mechanical Engineering
05 social sciences
lcsh:TA1001-1280
lcsh:HE1-9990
Computer Science Applications
Feature (computer vision)
Automotive Engineering
020201 artificial intelligence & image processing
Artificial intelligence
lcsh:Transportation engineering
lcsh:Transportation and communications
business
Subjects
Details
- ISSN :
- 20423195 and 01976729
- Volume :
- 2018
- Database :
- OpenAIRE
- Journal :
- Journal of Advanced Transportation
- Accession number :
- edsair.doi.dedup.....a837ba26ef0afca2b73c003bb302fa2b