ExoNet Database: Wearable Camera Images of Human Locomotion Environments

Abstract: Advances in computer vision and artificial intelligence are allowing researchers to develop environment recognition systems for powered lower-limb exoskeletons and prostheses. However, small-scale and private training datasets have impeded the widespread development and dissemination of image classification algorithms for classifying human walking environments. To address these limitations, we developed “ExoNet” - the first open-source, large-scale hierarchical database of high-resolution wearable camera images of human locomotion environments. Unparalleled in scale and diversity, ExoNet contains over 5.6 million RGB images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable camera system throughout the summer, fall, and winter seasons. Approximately 923,000 images in ExoNet were human-annotated using a 12-class hierarchical labelling architecture. Available publicly through IEEE DataPort, ExoNet offers an unprecedented communal platform to train, develop, and compare next-generation image classification algorithms for human locomotion environment recognition. Besides the control of powered lower-limb exoskeletons and prostheses, applications of ExoNet could extend to humanoids and autonomous legged robots.Reference: Laschowski B, McNally W, Wong A, and McPhee J. (2020). ExoNet Database: Wearable Camera Images of Human Locomotion Environments. Frontiers in Robotics and AI. DOI: 10.3389/frobt.2020.562061.

摘要：计算机视觉（computer vision）与人工智能（artificial intelligence）的进展使研究人员能够开发用于动力式下肢外骨骼与假肢（powered lower-limb exoskeletons and prostheses）的环境识别系统。然而，小规模且私有性质的训练数据集阻碍了用于人类行走环境（human locomotion environments）分类的图像分类算法（image classification algorithms）的广泛开发与传播。为解决这些局限，我们构建了"ExoNet"——首个开源、大规模的分层数据库，包含高分辨率可穿戴相机系统（wearable camera system）拍摄的人类行走环境图像。其规模与多样性均无可比拟，ExoNet涵盖560多万张不同室内外真实行走环境的RGB图像（RGB images），这些图像通过轻量级可穿戴相机系统在夏、秋、冬三季采集。其中约92.3万张图像采用12类分层标注架构（hierarchical labelling architecture）进行人工标注。通过IEEE DataPort公开提供，ExoNet为训练、开发和比较下一代人类行走环境识别图像分类算法提供了前所未有的公共平台。除用于动力式下肢外骨骼与假肢的控制外，ExoNet的应用还可拓展至人形机器人（humanoids）与自主式腿足机器人（autonomous legged robots）领域。

参考文献：Laschowski B、McNally W、Wong A及McPhee J.（2020）。ExoNet数据库：人类行走环境的可穿戴相机图像。《机器人学与人工智能前沿》（Frontiers in Robotics and AI）。DOI：10.3389/frobt.2020.562061。