A global dataset of continuous urban dashcam driving

Md Shadab Alam; Olena Bazilinska; Pavlo Bazilinskyy

cs.CVApr 1, 2026

A global dataset of continuous urban dashcam driving

Authors: Md Shadab Alam, Olena Bazilinska, Pavlo Bazilinskyy

Organizations: Eindhoven University of Technology, Eindhoven, The Netherlands · National University of Kyiv-Mohyla Academy, Kyiv, Ukraine

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.

Figures & tables

Figure 1: Geographic coverage of CROWD across 7,103 localities worldwide. Each marker denotes a locality with at least one retained dashcam segment. Red markers indicate cities with more than 100,000 s of retained footage (n = 112). Insets show selected example localities linked to their positions on the map.

Autonomous Driving Datasets

Autonomous driving datasets collect and organize driving-scene data to train and evaluate perception, prediction, planning, and safety systems.

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BMD-45: A Large-Scale CCTV Vehicle Detection Dataset for Urban Traffic in Developing Cities

Akash Sharma, Chinmay Mhatre, Sankalp Gawali, Ruthvik Bokkasam+7

Department of Computational and Data Sciences (CDS) Indian Institute of Science, Bengaluru, India · Centre for Infrastructure, Sustainable Transportation & Urban Planning (CiSTUP) Indian Institute of Science, Bengaluru, India · Center of Data for Public Good (CDPG) Indian Institute of Science, Bengaluru, India+1

Distribution Shift Object Detection Video Surveillance

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Robust vehicle detection from fixed CCTV cameras is critical for Intelligent Transportation Systems. Yet existing benchmarks predominantly feature relatively homogeneous, highly organized traffic patterns captured from ego-centric driving perspectives or controlled aerial views. This regional and sensor view bias creates a significant gap. Models trained on datasets such as UA-DETRAC and COCO struggle to generalize to the dense, heterogeneous, disorganized traffic conditions observed in rapidly developing urban centers in emerging economies. To address this limitation, we introduce BMD-45, a large-scale dataset comprising 480K bounding boxes annotated over 45K images captured from over 3.6K operational Safe City CCTV cameras. BMD-45 contains 14 fine-grained vehicle categories, including region-specific modes such as auto-rickshaws and tempo travellers, which are not present in existing benchmarks. The dataset captures real-world deployment challenges, including extreme viewpoint variation, occlusion, and vehicle density . We establish comprehensive baselines using state-of-the-art detectors and reveal a striking domain gap: models fine-tuned on UA-DETRAC achieve only 33.6% [email protected]:0.95, compared to 83.8% when trained in-domain on BMD-45, representing a 2.5x improvement that persists even when accounting for novel vehicle classes. This performance gap underscores the critical need for geographically diverse traffic benchmarks and establishes BMD-45 as a baseline for developing robust perception systems in underrepresented urban environments worldwide. The dataset is available at: https://huggingface.co/datasets/iisc-aim/BMD-45.

Abstract

Autonomous Driving Datasets

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