Research Article
Automated Pedestrians Data Collection Using Computer Vision
@INPROCEEDINGS{10.1007/978-3-319-33681-7_3, author={Tarek Sayed and Mohamed Zaki and Ahmed Tageldin}, title={Automated Pedestrians Data Collection Using Computer Vision}, proceedings={Smart City 360°. First EAI International Summit, Smart City 360°, Bratislava, Slovakia and Toronto, Canada, October 13-16, 2015. Revised Selected Papers}, proceedings_a={SMARTCITY360}, year={2016}, month={6}, keywords={Pedestrian data collection Computer vision Road safety Surrogate measures}, doi={10.1007/978-3-319-33681-7_3} }- Tarek Sayed
Mohamed Zaki
Ahmed Tageldin
Year: 2016
Automated Pedestrians Data Collection Using Computer Vision
SMARTCITY360
Springer
DOI: 10.1007/978-3-319-33681-7_3
Abstract
Active modes of travel such as walking are being encouraged in many cities to mitigate traffic congestion and to provide health and environmental benefits. However, the physical vulnerability of pedestrians may expose them to severe consequences when involved in traffic collisions. This paper presents three applications for automated video analysis of pedestrian behavior. The first is a methodology to detect distracted pedestrians on crosswalks using their gait parameters. The methodology utilizes recent findings in health science concerning the relationship between walking gait behavior and cognitive abilities. In the second application, a detection procedure for pedestrian violations is presented. In this procedure, spatial and temporal crossing violations are detected based on pattern matching. The third study addresses the problem of identifying pedestrian evasive actions. An effective method based on time series analysis of the walking profile is used to characterize the evasive actions. The results in the three applications show satisfactory accuracy. This research is beneficial for improving the design of pedestrian facilities to promote pedestrian safety and walkability.
