Problem

Pedestrians (particularly older adults and people with disabilities) can face safety and usability challenges at street crossings because of the inherent risks associated with environmental conditions (accumulations of rain, snow, ice, or poor lighting) and through their interactions with cyclists and vehicles. These external factors have both physical and psychological consequences on the perceived safety and risk of fall and injury. As a result, older adults and people with disabilities may choose to reduce their outdoor activity levels, isolating themselves in their homes, and potentially causing a downward spiral of deconditioning, frailty, and physical decline. Weakened physical strength can then increases their risk of falling and sustaining an injury in the future, which would subsequently perpetuating the fear that keeps them inside and continue this cycle of fear and limited movement in the winter. The development of more pedestrian-centric infrastructure can help to promote active transportation and reduce the winter isolation that so many older adults and persons with disabilities experience.

Currently, there is a lack of quantitative observational methods for pedestrians which has limited the understanding of the impacts that environmental barriers and other road users have on older adults and persons with disabilities. This project involves the development and evaluation of computer vision tools to investigate pedestrian safety and usability challenges at intersections. These methods will highlight limitations in current infrastructure design and maintenance so that elements of the street crossing can be made safer and more usable for all.

Goal

The objective of this project is to automate the assessment of pedestrian behaviour at street crossings in urban areas to help understand the challenges that pedestrians face, especially individuals with the greatest safety or usability concerns. Computer vision approaches including the You Only Look Once (YOLOv7) and Simple, Online, and Real-time Tracking (DeepSORT) will be used to automate the detection and measurement of pedestrian walking behaviour, as well as the classification of conflicts between pedestrians and other road users to achieve the following aims:

1. Develop a portable camera system for recording video and integrate with YOLOv7 and DeepSORT.
2. Demonstrate the ability of computer vision techniques to identify weather-related pedestrian usability challenges.
3. Identify interactions and conflicts between pedestrians and other road users at street crossings.
4. Propose a method for assessing the accessibility of street crossing for pedestrians to compare the infrastructure of intersections within the City of Toronto. This intersection safety scoring system will work with urban planning experts in industry and academia to weigh the impacts of external risk factors on pedestrian movement.

This project will provide new automated observation tools (a) for evaluating the safety and usability of street crossings and (b) to understand the complex relationship between pedestrians and the built environment. We hope to encourage the development of solutions that promote active mobility, allowing older adults and persons with disabilities to access their daily necessities, and fulfill their sense of autonomy, and social connection to your surrounding community.

Results

This research is currently ongoing and results are not yet available.