The OpenPack dataset is the largest publicly available dataset for recognizing complex packaging work activities in industrial logistics environments. It contains 53.8 hours of multimodal sensor data, including acceleration, gyroscope, depth images, LiDAR point clouds, and readings from IoT devices like barcode scanners, collected from 16 subjects with varying levels of packaging experience.
The dataset provides 10 classes of packaging operations and 32 action classes, with rich metadata on the subjects and order details. This enables research on advanced activity recognition methods that can leverage contextual information beyond just sensor data.
The benchmark results show that existing state-of-the-art models struggle to achieve high accuracy, especially in challenging scenarios with variations in working speed, item characteristics, and occlusions. This highlights the need for developing speed-invariant, metadata-aided, and multi-modal fusion techniques to enable robust recognition of complex industrial work activities.
OpenPack presents opportunities for various research directions, including transfer learning, skill assessment, mistake detection, and fatigue estimation, in addition to the core task of activity recognition. The dataset is expected to contribute significantly to advancing sensor-based work activity recognition in industrial domains.
다른 언어로
소스 콘텐츠 기반
arxiv.org
핵심 통찰 요약
by Naoya Yoshim... 게시일 arxiv.org 04-23-2024
https://arxiv.org/pdf/2212.11152.pdf더 깊은 질문