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 humanoid and autonomous legged robotics.Reference: Laschowski B, McNally W, Wong A, and McPhee J. (2020). ExoNet Database: Wearable Camera Images of Human Locomotion Environments. Frontiers in Robotics and Artificial Intelligence. Under Review.

摘要：计算机视觉与人工智能领域的技术进展，正助力研究人员为动力式下肢外骨骼（powered lower-limb exoskeletons）和假肢（prostheses）研发环境识别系统。然而，小规模且私有化的训练数据集，阻碍了用于识别人类行走环境的图像分类算法的广泛开发与推广应用。
为解决上述局限，我们构建了ExoNet——首个开源、大规模分层级的人类行走环境可穿戴相机高分辨率图像数据库。该数据集在规模与多样性方面均处于领先水平，包含超过560万张涵盖不同室内外真实行走场景的RGB图像，由轻量化可穿戴相机系统在夏、秋、冬三季采集完成。其中约92.3万张图像通过12类分层标注架构完成人工标注。
ExoNet可通过IEEE DataPort公开获取，为训练、开发及对比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研究平台。除应用于动力式下肢外骨骼与假肢的控制之外，ExoNet的应用场景还可拓展至类人机器人与自主腿部机器人领域。
参考文献：Laschowski B、McNally W、Wong A与McPhee J.（2020）. ExoNet数据库：人类行走环境可穿戴相机图像. 《机器人学与人工智能前沿（Frontiers in Robotics and Artificial Intelligence）》. 正在审稿中。