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进展，使得研究者能够研发面向动力下肢外骨骼（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，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。为解决上述局限，我们构建了ExoNet——首个开源、大规模分层式数据库，收录人类行走环境的高分辨率可穿戴相机图像。该数据集在规模与多样性上均无可比拟，包含超过560万张不同室内外真实行走场景的RGB图像（RGB images），采集过程覆盖夏、秋、冬三季，采用轻量化可穿戴相机系统完成。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数据端口（IEEE DataPort）公开获取，为训练、开发与对比用于人类行走环境识别的下一代图像分类算法提供了前所未有的公共研究平台。除应用于动力下肢外骨骼与假肢的控制场景外，ExoNet的适用场景还可拓展至人形机器人（humanoid）与自主腿式机器人（autonomous legged robotics）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数据集》.《机器人学与人工智能前沿》（Frontiers in Robotics and Artificial Intelligence），正在评审中。