A global dataset of continuous urban dashcam driving

Alam, M. S., Bazilinska, O., Bazilinskyy, P.

Submitted for publication.

ABSTRACT We introduce CROWD (City Road Observations With Dashcams), a manually curated dataset of ordinary, variable duration, 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 75,147 segment records spanning 26,273.75 hours (58,467 unique uploads), covering 8,049 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 reproducible baseline analysis, we provide per-segment CSV files of automatic detections for all 80 MS-COCO classes produced with YOLOv11x, together with segment-local multi-object tracks generated with 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 detection and tracking pseudo-labels, enabling reproducible research without redistributing the underlying videos.