The current study examines the possibility of automatically detecting distracted pedestrians on crosswalks using their gait parameters. The methodology utilises recent findings in health science concerning the relationship between walking gait behaviour and cognitive abilities. Walking speed and gait variability are shown to be affected by the complexity of tasks (e.g. texting) that are performed during walking. Experiments are performed on a video data set from Surrey, British Columbia. The analysis relies on automated video-based data collection using computer vision. A sensitivity analysis is carried out to assess the quality of the selected features in improving the accuracy of the classification. Classification results show that the proposed approach is promising with around 80% correct detection rate. This research can benefit applications in several transportation related fields such as pedestrian facility planning, pedestrian simulation models as well as road safety programmes and legislative studies.
Exploring walking gait features for the automated recognition of distracted pedestrians
IET Intelligent Transport Systems ; 10 , 2 ; 106-113
2016-03-01
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
video signal processing , image classification , automated recognition , video data set , computer vision , pedestrians , distracted pedestrians , walking gait features , sensitivity analysis , gait analysis , automated video-based data collection , object recognition , cognitive abilities , walking gait behaviour
Metadata by IET is licensed under CC BY 3.0
Exploring walking gait features for the automated recognition of distracted pedestrians
Wiley | 2016
|Distracted walking: Cell phones increase injury risk for college pedestrians
Online Contents | 2011
|Self-Reported Behaviors and Habits of Distracted College Pedestrians While Walking
Transportation Research Record | 2017
|Transportation Research Record | 2016
|