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OFFICE OF RESEARCH, DEVELOPMENT, AND TECHNOLOGY AT THE TURNER-FAIRBANK HIGHWAY RESEARCH CENTER

Exploratory Advanced Research (EAR) Program Compendium of Papers from Funded Research Projects

Publication Information

Publication Type:
Other
Publication Number:
FHWA-HRT-23-021
Abstract:

Researchers for this study developed improved video processing algorithms that can identify more complex behavior and actions of individuals (i.e., pedestrians, drivers, bicyclists) in various traffic situations. The research team wanted to build on existing video-processing algorithms that can identify basic behavior, “primitives” (such as when a subject’s hand is near their head), but cannot always identify a “high-level” behavior (such as talking on a cellphone). Specifically, the researchers aimed to:

 

  • Use a bottom-up machine-learning approach to train an algorithm to recognize high-level behaviors using object detection, human-pose estimation, and behavior classification.
  • Use a top-down approach to catalog primitive and high-level behaviors and develop a statistical prediction model to link them.

 


 

Recommended citation: Federal Highway Administration, Exploratory Advanced Research (EAR) Program Compendium of Papers from Funded Research Projects (Washington, DC: 2023) https://doi.org/10.21949/1521965.

Publishing Date:
January 30, 2023
Posting Date:
Digital Object Identifier:
https://doi.org/10.21949/1521965
Author(s):
Kreutzer, Analiese
Publishing Office:
Human Factors Team
FHWA Program(s):
Human Factors
AMRP Program(s):
Exploratory Advanced Research
FHWA Activities:
Human Factors
Pedestrian / Bicycle
Subject Area:
Pedestrians and Bicyclists