ExoNet Database: Wearable Camera Images of Human Locomotion Environments
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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, 7, 562061. DOI: 10.3389/frobt.2020.562061.
摘要：计算机视觉与人工智能领域的进展，使得研究者得以开发面向动力式下肢外骨骼与假肢的环境识别系统。然而，小规模且私有化的训练数据集，阻碍了用于人类行走环境分类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（ExoNet）——首款开源、大规模分层式人类行走环境可穿戴相机高清图像数据库。该数据库在规模与多样性上均无可比拟，包含超过560万张涵盖不同室内外真实行走环境的RGB图像，数据采集依托轻量化可穿戴相机系统完成，覆盖夏季、秋季与冬季三个季节。ExoNet中约92.3万张图像采用12类分层标注架构完成人工标注。ExoNet可通过IEEE数据港（IEEE DataPort）公开获取，为训练、开发与对比面向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。除应用于动力式下肢外骨骼与假肢的控制外，ExoNet的应用场景还可拓展至类人机器人与自主式足式机器人领域。参考文献：Laschowski B, McNally W, Wong A 及 McPhee J. (2020). 《ExoNet数据库：人类行走环境可穿戴相机图像》. 《机器人与人工智能前沿（Frontiers in Robotics and AI）》, 7, 562061. DOI: 10.3389/frobt.2020.562061.
