{"url":"/dataset/ilids-vid","name":"iLIDS-VID","full_name":"iLIDS-VID","description_markdown":"The **iLIDS-VID** dataset is a person re-identification dataset which involves 300 different pedestrians observed across two disjoint camera views in public open space. It comprises 600 image sequences of 300 distinct individuals, with one pair of image sequences from two camera views for each person. Each image sequence has variable length ranging from 23 to 192 image frames, with an average number of 73. The iLIDS-VID dataset is very challenging due to clothing similarities among people, lighting and viewpoint variations across camera views, cluttered background and random occlusions.\n\nSource: [http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html](http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html)\nImage Source: [http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html](http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html)","description_withheld":null,"homepage":"http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html","introduced_date":"2009-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/unsupervised-person-re-identification-by-deep-1","title":"Unsupervised Person Re-identification by Deep Learning Tracklet Association","first_author":"Minxian Li","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Person Re-Identification","url":"/task/person-re-identification","datasets_with_task":"/datasets/task/person-re-identification"},{"name":"Unsupervised Person Re-Identification","url":"/task/unsupervised-person-re-identification","datasets_with_task":"/datasets/task/unsupervised-person-re-identification"},{"name":"Video-Based Person Re-Identification","url":"/task/video-based-person-re-identification","datasets_with_task":"/datasets/task/video-based-person-re-identification"}],"languages":[],"variants":["iLIDS-VID"],"data_loaders":[],"num_papers_in_archive":20,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/person-re-identification-on-ilids-vid","task":"Person Re-Identification","dataset_variant":"iLIDS-VID","rows":10,"metrics":["Rank-1","Rank-5","Rank-10","Rank-20"],"first_row_in_archive_order":{"model":"GAF-Net","paper":"/paper/gaf-net-video-based-person-re-identification","metrics":{"Rank-1":"93.07","Rank-10":"99.74","Rank-20":"99.94","Rank-5":"99.27"},"code_links":[{"title":"Moncef-Bj/GAF-Net-for-Video-Based-Person-Re-Identification","url":"https://github.com/Moncef-Bj/GAF-Net-for-Video-Based-Person-Re-Identification"},{"title":"pwc-1/Paper-9","url":"https://github.com/pwc-1/Paper-9/tree/main/6/GAF"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/unsupervised-person-re-identification-on-10","task":"Unsupervised Person Re-Identification","dataset_variant":"iLIDS-VID","rows":1,"metrics":[" Rank-1","Rank-5","Rank-20"],"first_row_in_archive_order":{"model":"uPMnet","paper":"/paper/exploiting-robust-unsupervised-video-person","metrics":{" Rank-1":"63.1","Rank-20":"92.5","Rank-5":"81.9"},"code_links":[{"title":"deropty/uPMnet","url":"https://github.com/deropty/uPMnet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/gaf-net-video-based-person-re-identification","title":"GAF-Net: Video-Based Person Re-Identification via Appearance and Gait Recognitions","date":"2024-02-27","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/multi-direction-and-multi-scale-pyramid-in-1","title":"Multi-direction and Multi-scale Pyramid in Transformer for Video-based Pedestrian Retrieval","date":"2022-02-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/exploiting-robust-unsupervised-video-person","title":"Exploiting Robust Unsupervised Video Person Re-identification","date":"2021-11-09","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/spatio-temporal-representation-factorization","title":"Spatio-Temporal Representation Factorization for Video-based Person Re-Identification","date":"2021-07-25","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/learning-multi-granular-hypergraphs-for-video-1","title":"Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification","date":"2021-04-30","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pyramid-spatial-temporal-aggregation-for","title":"Pyramid Spatial-Temporal Aggregation for Video-Based Person Re-Identification","date":"2021-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/fine-grained-re-identification","title":"Fine-Grained Re-Identification","date":"2020-11-26","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/adaptive-graph-representation-learning-for","title":"Adaptive Graph Representation Learning for Video Person Re-identification","date":"2019-09-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/temporal-knowledge-propagation-for-image-to","title":"Temporal Knowledge Propagation for Image-to-Video Person Re-identification","date":"2019-08-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/unsupervised-tracklet-person-re","title":"Unsupervised Tracklet Person Re-Identification","date":"2019-03-01","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"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."}