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Title: Automated Analysis of Pedestrian–Vehicle Conflicts Using Video Data
Accession Number: 01125549
Record Type: Component
Record URL: Availability: Transportation Research Board Business Office 500 Fifth Street, NW Find a library where document is available Abstract: Pedestrians are vulnerable road users, and despite their limited representation in traffic events, pedestrian-involved injuries and fatalities are overrepresented in traffic collisions. However, little is known about pedestrian exposure to the risk of collision, especially when compared with the amount of knowledge available for motorized traffic. More data and analysis are therefore required to understand the processes that involve pedestrians in collisions. Collision statistics alone are inadequate for the study of pedestrian–vehicle collisions because of data quantity and quality issues. Surrogate safety measures, as provided by the collection and study of traffic conflicts, were developed as a proactive complementary approach to offer more in-depth safety analysis. However, high costs and reliability issues have inhibited the extensive application of traffic conflict analysis. An automated video analysis system is presented that can (a) detect and track road users in a traffic scene and classify them as pedestrians or motorized road users, (b) identify important events that may lead to collisions, and (c) calculate several severity conflict indicators. The system seeks to classify important events and conflicts automatically but can also be used to summarize large amounts of data that can be further reviewed by safety experts. The functionality of the system is demonstrated on a video data set collected over 2 days at an intersection in downtown Vancouver, British Columbia, Canada. Four conflict indicators are automatically computed for all pedestrian–vehicle events and provide detailed insight into the conflict process. Simple detection rules on the indicators are tested to classify traffic events. This study is unique in its attempt to extract conflict indicators from video sequences in a fully automated way.
Monograph Title: Monograph Accession #: 01149182
Report/Paper Numbers: 09-3743
Language: English
Authors: Ismail, KarimSayed, TarekSaunier, NicolasLim, ClarkPagination: pp 44-54
Publication Date: 2009
ISBN: 9780309142731
Media Type: Print
Features: Appendices
(1)
; Figures
(5)
; References
(36)
; Tables
(1)
TRT Terms: Uncontrolled Terms: Geographic Terms: Subject Areas: Pedestrians and Bicyclists; Safety and Human Factors; I83: Accidents and the Human Factor
Files: TRIS, TRB, ATRI
Created Date: Jan 30 2009 8:09PM
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