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.

翻译：步行一直是主要的交通方式，也被认为是维持健康的重要活动。尽管城市环境中需要安全的步行条件，但人行道经常被各种障碍物阻塞，阻碍行人的自由通行。任何阻碍行人通行的物体都可能构成安全隐患。普适计算和自我中心视觉技术的发展为设计能够实时自动检测此类障碍物的系统提供了可能，从而提升行人安全性。高效识别算法的开发依赖于全面且均衡的自我中心数据集的可用性。本研究提出了PEDESTRIAN数据集，包含城市人行道上常见的29种不同障碍物的自我中心数据。使用手机摄像头共采集了340段视频，捕捉行人的第一视角。此外，我们展示了一系列实验的结果，这些实验使用所提出的数据集训练了多种先进的深度学习算法，该数据集可作为障碍物检测与识别任务的基准。本数据集可用于训练人行道障碍物检测器，以提升城市区域的行人安全。