{"url":"/dataset/dronecrowd","name":"DroneCrowd","full_name":null,"description_markdown":"**DroneCrowd** is a benchmark for object detection, tracking and counting algorithms in drone-captured videos. It is a drone-captured large scale dataset formed by 112 video clips with 33,600 HD frames in various scenarios. Notably, it has annotations for 20,800 people trajectories with 4.8 million heads and several video-level attributes.","description_withheld":null,"homepage":"https://github.com/VisDrone/DroneCrowd","introduced_date":"2021-05-06","introduced_date_note":null,"introduced_by":{"paper":"/paper/detection-tracking-and-counting-meets-drones","title":"Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark","first_author":"Longyin Wen","url":null},"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Crowd Counting","url":"/task/crowd-counting","datasets_with_task":"/datasets/task/crowd-counting"},{"name":"drone-based object tracking","url":"/task/drone-based-object-tracking","datasets_with_task":"/datasets/task/drone-based-object-tracking"}],"languages":[],"variants":["DroneCrowd"],"data_loaders":[{"repo":"https://github.com/VisDrone/DroneCrowd","url":"https://github.com/VisDrone/DroneCrowd","frameworks":["pytorch"]}],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}