{"url":"/dataset/aerialmpt","name":"AerialMPT","full_name":null,"description_markdown":"AerialMPT is a dataset for pedestrian tracking in aerial image sequences and presents real-world challenges for MOT algorithms such as low frame rate, small moving objects, and complex backgrounds. AerialMPT consists of 14 sequences and 307 frames with an average size of 425 × 358 pixels. The images were acquired by DLR's 4K camera system from altitudes ranging from 600 m to 1400 m, resulting in spatial resolutions (GSDs) ranging from 8 cm/pixel to 13 cm/pixel. In a post-processing step, the images were co-registered, geo-referenced, and cropped for each region of interest, resulting in sequences of 2 fps. The images were acquired during different flight campaigns between 2016 and 2017, over different scenes containing pedestrians and with different crowd densities and movement complexities.","description_withheld":null,"homepage":"https://www.dlr.de/en/eoc/about-us/remote-sensing-technology-institute/photogrammetry-and-image-analysis/public-datasets/aerialmpt-a-dataset-for-pedestrian-tracking-in-aerial-imagery","introduced_date":"2021-05-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/aerialmptnet-multi-pedestrian-tracking-in","title":"AerialMPTNet: Multi-Pedestrian Tracking in Aerial Imagery Using Temporal and Graphical Features","first_author":"Maximilian Kraus","url":null},"license":{"name":"CC BY-SA 4.0","url":"https://creativecommons.org/licenses/by-sa/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Multi-Object Tracking","url":"/task/multi-object-tracking","datasets_with_task":"/datasets/task/multi-object-tracking"}],"languages":[],"variants":["AerialMPT"],"data_loaders":[],"num_papers_in_archive":1,"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."}