A global dataset of continuous urban dashcam driving

TL;DR

CROWD is a large, diverse urban dashcam dataset from YouTube videos, supporting robustness and interaction analysis with manual labels and machine-generated detections, facilitating reproducible research.

Contribution

The paper introduces CROWD, a comprehensive, curated urban dashcam dataset with detailed annotations and machine detection data, enabling cross-domain robustness studies.

Findings

Contains 51,753 segments from over 20,000 hours of footage.

Covers 7,103 locations across all inhabited continents.

Provides machine-generated detections for 80 object classes.

Abstract

We introduce CROWD (City Road Observations With Dashcams), a manually curated dataset of ordinary, minute scale, temporally contiguous, unedited, front facing urban dashcam segments screened and segmented from publicly available YouTube videos. CROWD is designed to support cross-domain robustness and interaction analysis by prioritising routine driving and explicitly excluding crashes, crash aftermath, and other edited or incident-focused content. The release contains 51,753 segment records spanning 20,275.56 hours (42,032 videos), covering 7,103 named inhabited places in 238 countries and territories across all six inhabited continents (Africa, Asia, Europe, North America, South America and Oceania), with segment level manual labels for time of day (day or night) and vehicle type. To lower the barrier for benchmarking, we provide per-segment CSV files of machine-generated detections for all 80 MS-COCO classes produced with YOLOv11x, together with segment-local multi-object tracks (BoT-SORT); e.g. person, bicycle, motorcycle, car, bus, truck, traffic light, stop sign, etc. CROWD is distributed as video identifiers with segment boundaries and derived annotations, enabling reproducible research without redistributing the underlying videos.