论文标题
OpenPACK：一个大型数据集，用于识别IOT启用逻辑环境中的包装作品
OpenPack: A Large-scale Dataset for Recognizing Packaging Works in IoT-enabled Logistic Environments
论文作者
论文摘要
与人类的日常活动不同，在工业领域中，现有的公开可用的传感器数据集以与工业站点的密切合作，在收集现实数据方面遇到的工作活动识别受到限制。这也限制了有关工业应用方法的研究和开发。为了应对这些挑战，并有助于研究工业领域中工作活动的机器识别，我们引入了一个新的大型数据集，以包装工作识别openpack。 OpenPack包含53.8个小时的多模式传感器数据，包括加速度数据，关键点，深度图像以及来自IoT启用设备的读数（例如，手持式条形码扫描仪），这些设备从具有不同级别的包装工作经验的16个不同受试者收集。我们将最新的人类活动识别技术应用于数据集，并根据结果在普遍的计算社区中提供复杂的工作活动识别研究的未来方向。我们认为，OpenPack将通过提供具有挑战性的任务来为基于传感器的动作/活动识别社区做出贡献。 OpenPack数据集可在https://open-pack.github.io上找到。
Unlike human daily activities, existing publicly available sensor datasets for work activity recognition in industrial domains are limited by difficulties in collecting realistic data as close collaboration with industrial sites is required. This also limits research on and development of methods for industrial applications. To address these challenges and contribute to research on machine recognition of work activities in industrial domains, in this study, we introduce a new large-scale dataset for packaging work recognition called OpenPack. OpenPack contains 53.8 hours of multimodal sensor data, including acceleration data, keypoints, depth images, and readings from IoT-enabled devices (e.g., handheld barcode scanners), collected from 16 distinct subjects with different levels of packaging work experience. We apply state-of-the-art human activity recognition techniques to the dataset and provide future directions of complex work activity recognition studies in the pervasive computing community based on the results. We believe that OpenPack will contribute to the sensor-based action/activity recognition community by providing challenging tasks. The OpenPack dataset is available at https://open-pack.github.io.
