{"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/the-unmanned-aerial-vehicle-benchmark-object","title":"The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking","arxiv_id":"1804.00518","date":"2018-03-26","proceeding":"ECCV 2018 9","authors":["Dawei Du","Yuankai Qi","Hongyang Yu","Yifan Yang","Kaiwen Duan","Guorong Li","Weigang Zhang","Qingming Huang","Qi Tian"],"abstract":"With the advantage of high mobility, Unmanned Aerial Vehicles (UAVs) are used\nto fuel numerous important applications in computer vision, delivering more\nefficiency and convenience than surveillance cameras with fixed camera angle,\nscale and view. However, very limited UAV datasets are proposed, and they focus\nonly on a specific task such as visual tracking or object detection in\nrelatively constrained scenarios. Consequently, it is of great importance to\ndevelop an unconstrained UAV benchmark to boost related researches. In this\npaper, we construct a new UAV benchmark focusing on complex scenarios with new\nlevel challenges. Selected from 10 hours raw videos, about 80,000\nrepresentative frames are fully annotated with bounding boxes as well as up to\n14 kinds of attributes (e.g., weather condition, flying altitude, camera view,\nvehicle category, and occlusion) for three fundamental computer vision tasks:\nobject detection, single object tracking, and multiple object tracking. Then, a\ndetailed quantitative study is performed using most recent state-of-the-art\nalgorithms for each task. Experimental results show that the current\nstate-of-the-art methods perform relative worse on our dataset, due to the new\nchallenges appeared in UAV based real scenes, e.g., high density, small object,\nand camera motion. To our knowledge, our work is the first time to explore such\nissues in unconstrained scenes comprehensively.","url_abs":"http://arxiv.org/abs/1804.00518v1","url_pdf":"http://arxiv.org/pdf/1804.00518v1.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":"multiple-object-tracking","task_name":"Multiple Object Tracking"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"visual-tracking","task_name":"Visual Tracking"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[{"slug":"uavdt","name":"UAVDT","full_name":"Unmanned Aerial Vehicle Benchmark Object Detection and Tracking"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-detection-on-uavdt","task":"Object Detection","dataset":"UAVDT","model":"R-FCN","rank_in_archive_order":5,"of":8,"metrics":{"mAP":"34.35"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-uavdt","task":"Object Detection","dataset":"UAVDT","model":"SSD","rank_in_archive_order":6,"of":8,"metrics":{"mAP":"33.62"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-uavdt","task":"Object Detection","dataset":"UAVDT","model":"Faster-RCNN","rank_in_archive_order":7,"of":8,"metrics":{"mAP":"22.32"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-uavdt","task":"Object Detection","dataset":"UAVDT","model":"RON","rank_in_archive_order":8,"of":8,"metrics":{"mAP":"21.59"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1804.00518","atlas_url":"https://app.syntology.ai/?focus=1804.00518","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}