Pedestrian Planet: What YouTube Driving from 233 Countries and Territories Teaches Us About the World

Figure 1: The 233 countries and territories with dashcam footage in CROWD dataset [2].The colouration is based on the logarithm of the total recorded time per country or territory, calculated as log (1 + time in seconds), to reduce the skew from outliers such as the United States (with 707.76 hours available).The black dots show the 2,495 cities in the dataset.The labels under images show the corresponding YouTube video ID.The frame on the bottom left shows an example of object detection using YOLOv11x with identified objects such as pedestrians, vehicles, and traffic signs; in this image, the labels 'id' refer to the unique ID of the detected object with the type mentioned later and end with the confidence of detection of the object.

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Publication Details

Published
2025-09-21
DOI
https://doi.org/10.1145/3744333.3747827
Citations
2
Primary Topic
Evacuation and Crowd Dynamics
Type
article
Field-Weighted Citation Impact
2.29
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article

Pedestrian Planet: What YouTube Driving from 233 Countries and Territories Teaches Us About the World

Marieke Martens, Pavlo Bazilinskyy, Md Shadab Alam
2 citations
Evacuation and Crowd Dynamics
2.29
article

Pedestrian Planet: What YouTube Driving from 233 Countries and Territories Teaches Us About the World

Marieke Martens, Pavlo Bazilinskyy, Md Shadab Alam
article en
2 citations

Abstract

Figure 1: The 233 countries and territories with dashcam footage in CROWD dataset [2].The colouration is based on the logarithm of the total recorded time per country or territory, calculated as log (1 + time in seconds), to reduce the skew from outliers such as the United States (with 707.76 hours available).The black dots show the 2,495 cities in the dataset.The labels under images show the corresponding YouTube video ID.The frame on the bottom left shows an example of object detection using YOLOv11x with identified objects such as pedestrians, vehicles, and traffic signs; in this image, the labels 'id' refer to the unique ID of the detected object with the type mentioned later and end with the confidence of detection of the object.

Design Academy Eindhoven (NL), Eindhoven University of Technology (NL)
Openalex Percentile: Top 11%
Evacuation and Crowd Dynamics
2.29
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