{"url":"/dataset/lmot","name":"LMOT","full_name":"Low-light Multi-object Tracking Dataset","description_markdown":"The **Low-light Multi-object Tracking Dataset (LMOT)** is a large-scale dataset that focuses on multi-object tracking in dark scenes. It consists of two parts: 1) The low-light and well-lit videos captured by our dual-camera system. 2) The real world low-light videos captured by a simple camera, to evaluate the generalization in real night scenarios. The videos are provided in both RAW format and sRGB format. After careful annotation, we collect 32 video sequences (2.3\\times MOT17), over 35K frames (3.1 \\times MOT17) and over 815K bounding boxes (2.8 \\times MOT17).","description_withheld":null,"homepage":"https://github.com/xinzwang/LMOT","introduced_date":"2024-05-10","introduced_date_note":null,"introduced_by":{"paper":"/paper/multi-object-tracking-in-the-dark","title":"Multi-Object Tracking in the Dark","first_author":"Xinzhe Wang","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["LMOT"],"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."}