构建了29类城市人行道障碍物的自拍视角数据集，助力行人安全导航。
PEDESTRIAN: An Egocentric Vision Dataset for Obstacle Detection on Pavements
- 采集29类常见障碍物的手机视角视频，覆盖真实步行场景。
- 包含340段视频，支持深度学习模型训练与检测性能对比。
- 适合研究行人安全、自动驾驶感知或智能助行系统的人群使用。
步行是主要出行方式之一，对健康至关重要。然而，城市人行道常被各类障碍物阻塞，威胁行人安全。随着普适计算和自拍视角视觉技术的发展，实时自动检测障碍物成为可能。高效算法的开发依赖于全面且均衡的自拍视角数据集。本文提出PEDESTRIAN数据集，包含29种常见城市人行道障碍物的340段移动设备拍摄的视频，呈现行人第一视角。我们还基于该数据集训练并评估了多种先进深度学习模型，可作为障碍物检测与识别任务的基准。该数据集可用于训练路面障碍物检测器，提升城市行人安全。
原文摘要 · Abstract (English)
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.
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