{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/youtube-boundingboxes-a-large-high-precision","title":"YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video","arxiv_id":"1702.00824","date":"2017-02-02","proceeding":"CVPR 2017 7","authors":["Esteban Real","Jonathon Shlens","Stefano Mazzocchi","Xin Pan","Vincent Vanhoucke"],"abstract":"We introduce a new large-scale data set of video URLs with densely-sampled\nobject bounding box annotations called YouTube-BoundingBoxes (YT-BB). The data\nset consists of approximately 380,000 video segments about 19s long,\nautomatically selected to feature objects in natural settings without editing\nor post-processing, with a recording quality often akin to that of a hand-held\ncell phone camera. The objects represent a subset of the MS COCO label set. All\nvideo segments were human-annotated with high-precision classification labels\nand bounding boxes at 1 frame per second. The use of a cascade of increasingly\nprecise human annotations ensures a label accuracy above 95% for every class\nand tight bounding boxes. Finally, we train and evaluate well-known deep\nnetwork architectures and report baseline figures for per-frame classification\nand localization to provide a point of comparison for future work. We also\ndemonstrate how the temporal contiguity of video can potentially be used to\nimprove such inferences. Please see the PDF file to find the URL to download\nthe data. We hope the availability of such large curated corpus will spur new\nadvances in video object detection and tracking.","url_abs":"http://arxiv.org/abs/1702.00824v5","url_pdf":"http://arxiv.org/pdf/1702.00824v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"video-object-detection","task_name":"Video Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[{"slug":"youtube-boundingboxes","name":"YT-BB","full_name":"YouTube-BoundingBoxes"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1702.00824","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}