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.

搜集汇总

背景与挑战

背景概述

ExoNet是目前最大的开源可穿戴相机图像数据集，包含560万张室内外行走环境的RGB图像，覆盖夏秋冬三季，其中92.3万张图像采用12类分层标签进行人工标注。该数据集为开发环境自适应控制的深度学习模型提供了重要平台。