EgoMAGIC- 一种用于训练感知算法的自拍视频医学数据集
EgoMAGIC- An Egocentric Video Field Medicine Dataset for Training Perception Algorithms
AI总结：
本文介绍EgoMAGIC数据集，包含50项医学任务的3355个视频，用于训练感知算法，通过挑战赛推动研究，提供124种医学物体的检测基准。
摘要：
本文介绍了EgoMAGIC（Medical Assistance, Guidance, Instruction, and Correction），DARPA PTG项目的一部分，包含50项医学任务的3355个视频，每个任务至少50个标注视频。该数据集用于训练虚拟助手，通过头戴式立体相机录制，训练40个YOLO模型，检测124种医学物体。本文还展示了三种模型在八项医学任务上的动作检测基准结果，最佳方法的平均mAP为0.526。该数据集也适用于动作识别、物体识别和检测等挑战性任务。数据集可通过zenodo.org (DOI: 10.5281/zenodo.19239154)访问。
英文摘要：
This paper introduces EgoMAGIC (Medical Assistance, Guidance, Instruction, and Correction), an egocentric medical activity dataset collected as part of DARPA's Perceptually-enabled Task Guidance (PTG) program. This dataset comprises 3,355 videos of 50 medical tasks, with at least 50 labeled videos per task. The primary objective of the PTG program was to develop virtual assistants integrated into augmented reality headsets to assist users in performing complex tasks. To encourage exploration and research using this dataset, the medical training data has been released along with an action detection challenge focused on eight medical tasks. The majority of the videos were recorded using a head-mounted stereo camera with integrated audio. From this dataset, 40 YOLO models were trained using 1.95 million labels to detect 124 medical objects, providing a robust starting point for developers working on medical AI applications. In addition to introducing the dataset, this paper presents baseline results on action detection for the eight selected medical tasks across three models, with the best-performing method achieving average mAP 0.526. Although this paper primarily addresses action detection as the benchmark, the EgoMAGIC dataset is equally suitable for action recognition, object identification and detection, error detection, and other challenging computer vision tasks. The dataset is accessible via zenodo.org (DOI: 10.5281/zenodo.19239154).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
