PEDESTRIAN: 一个用于人行道障碍物检测的眼动视觉数据集
PEDESTRIAN: An Egocentric Vision Dataset for Obstacle Detection on Pavements
AI总结：
本文提出PEDESTRIAN数据集，用于训练深度学习模型检测人行道上的障碍物，提升城市行人安全。
摘要：
步行一直是主要的交通方式，并被认为是维持良好健康的重要活动。尽管城市环境中对安全步行条件有需求，人行道经常被各种障碍物阻塞，阻碍了自由的行人移动。任何阻碍行人路径的对象都可能构成安全威胁。普及计算和眼动视觉技术的进步提供了设计能够实时自动检测此类障碍物的系统的潜力，从而提高行人的安全。有效且高效的识别算法的发展依赖于全面且平衡的眼动数据集的可用性。在本文中，我们介绍了PEDESTRIAN数据集，包含29种常见于城市人行道的眼动数据。总共收集了340个视频，使用手机摄像头拍摄行人的视角。此外，我们还展示了涉及使用所提出数据集训练多种先进深度学习算法的一系列实验结果，这些结果可以作为障碍物检测和识别任务的基准。该数据集可用于训练人行道障碍物检测器，以提高城市地区行人的安全。
英文摘要：
Walking has always been a primary mode of transportation and is recognized as an essential activity for maintaining good health. Despite the need for safe walking conditions in urban environments, sidewalks are frequently obstructed by various obstacles that hinder free pedestrian movement. Any object obstructing a pedestrian's path can pose a safety hazard. The advancement of pervasive computing and egocentric vision techniques offers the potential to design systems that can automatically detect such obstacles in real time, thereby enhancing pedestrian safety. The development of effective and efficient identification algorithms relies on the availability of comprehensive and well-balanced datasets of egocentric data. In this work, we introduce the PEDESTRIAN dataset, comprising egocentric data for 29 different obstacles commonly found on urban sidewalks. A total of 340 videos were collected using mobile phone cameras, capturing a pedestrian's point of view. Additionally, we present the results of a series of experiments that involved training several state-of-the-art deep learning algorithms using the proposed dataset, which can be used as a benchmark for obstacle detection and recognition tasks. The dataset can be used for training pavement obstacle detectors to enhance the safety of pedestrians in urban areas.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
