ExoNet Database: Wearable Camera Images of Human Locomotion Environments

Abstract: Recent 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 this limitation, 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 images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable smartphone camera 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. Beyond the control of powered lower-limb exoskeletons and prostheses, prospective applications of ExoNet 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最新进展，促使研究者得以研发面向动力式下肢外骨骼（powered lower-limb exoskeletons）与假肢（prostheses）的环境识别系统。然而，小规模且私有化的训练数据集，掣肘了用于识别人类行走环境的图像分类算法的规模化开发与传播推广。为解决这一局限，我们构建了ExoNet——首个开源的大规模层级化人类运动环境可穿戴相机图像数据库。该数据库在规模与多样性上均无可匹敌，收录超560万张涵盖不同室内外真实行走环境的图像，采集工作依托轻量化可穿戴智能手机相机完成，覆盖夏、秋、冬三季。ExoNet中约92.3万张图像采用12类层级标注架构完成人工标注。该数据库可通过IEEE DataPort（IEEE数据端口）公开获取，为训练、开发与对比用于人类运动环境识别的下一代图像分类算法提供了前所未有的共享平台。除应用于动力式下肢外骨骼与假肢的控制之外，ExoNet的潜在应用场景还可拓展至人形机器人与自主腿部机器人领域。

参考文献：Laschowski B、McNally W、Wong A 及 McPhee J.（2020）. ExoNet 数据库：人类运动环境可穿戴相机图像集. 《Frontiers in Robotics and Artificial Intelligence》（机器人学与人工智能前沿），审稿中。