{"url":"/dataset/ua-detrac","name":"UA-DETRAC","full_name":null,"description_markdown":"Consists of 100 challenging video sequences captured from real-world traffic scenes (over 140,000 frames with rich annotations, including occlusion, weather, vehicle category, truncation, and vehicle bounding boxes) for object detection, object tracking and MOT system. \r\n\r\nSource: [UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking](/paper/ua-detrac-a-new-benchmark-and-protocol-for)\r\n\r\nImage Source: [UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking](/paper/ua-detrac-a-new-benchmark-and-protocol-for)","description_withheld":null,"homepage":"","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/ua-detrac-a-new-benchmark-and-protocol-for","title":"UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking","first_author":"Longyin Wen","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"name":"Object Tracking","url":"/task/object-tracking","datasets_with_task":"/datasets/task/object-tracking"},{"name":"Multiple Object Tracking","url":"/task/multiple-object-tracking","datasets_with_task":"/datasets/task/multiple-object-tracking"}],"languages":[],"variants":["UA-DETRAC"],"data_loaders":[{"repo":"https://github.com/xdtyjwj/datesets_of_UATRAC","url":"https://github.com/xdtyjwj/datesets_of_UATRAC","frameworks":["tf","pytorch","jax"]}],"num_papers_in_archive":53,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/object-detection-on-ua-detrac","task":"Object Detection","dataset_variant":"UA-DETRAC","rows":9,"metrics":["mAP"],"first_row_in_archive_order":{"model":"VSTAM","paper":"/paper/video-sparse-transformer-with-attention","metrics":{"mAP":"90.39"},"code_links":[{"title":"Malik1998/VSTAM","url":"https://github.com/Malik1998/VSTAM"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multiple-object-tracking-on-ua-detrac","task":"Multiple Object Tracking","dataset_variant":"UA-DETRAC","rows":1,"metrics":["MOTA"],"first_row_in_archive_order":{"model":"EB & TADN","paper":"/paper/transformer-based-assignment-decision-network","metrics":{"MOTA":"23.7"},"code_links":[{"title":"psaltaath/tadn-mot","url":"https://github.com/psaltaath/tadn-mot"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/transformer-based-assignment-decision-network","title":"Transformer-based assignment decision network for multiple object tracking","date":"2022-08-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/video-sparse-transformer-with-attention","title":"Video Sparse Transformer With Attention-Guided Memory for Video Object Detection","date":"2022-06-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ffavod-feature-fusion-architecture-for-video","title":"FFAVOD: Feature Fusion Architecture for Video Object Detection","date":"2021-09-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/rn-vid-a-feature-fusion-architecture-for","title":"RN-VID: A Feature Fusion Architecture for Video Object Detection","date":"2020-03-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/spotnet-self-attention-multi-task-network-for","title":"SpotNet: Self-Attention Multi-Task Network for Object Detection","date":"2020-02-13","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/objects-as-points","title":"Objects as Points","date":"2019-04-16","rows_on_this_dataset":1,"code_links":76,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":130,"samples_ran":10,"samples_unverified":120,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/3d-detnet-a-single-stage-video-based-vehicle","title":"3D-DETNet: a Single Stage Video-Based Vehicle Detector","date":"2018-01-05","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/yolo9000-better-faster-stronger","title":"YOLO9000: Better, Faster, Stronger","date":"2016-12-25","rows_on_this_dataset":1,"code_links":231,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":60,"samples_ran":16,"samples_unverified":44,"pointer_only_for_licence":22,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/r-fcn-object-detection-via-region-based-fully","title":"R-FCN: Object Detection via Region-based Fully Convolutional Networks","date":"2016-05-20","rows_on_this_dataset":1,"code_links":48,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":1,"samples_unverified":10,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/faster-r-cnn-towards-real-time-object","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","date":"2015-06-04","rows_on_this_dataset":1,"code_links":196,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":124,"samples_ran":59,"samples_unverified":65,"pointer_only_for_licence":42,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":325,"samples_ran":86,"samples_unverified":239,"pointer_only_for_licence":64,"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."}