PEDESTRIAN: An Egocentric Vision Dataset for Obstacle Detection on Pavements

TL;DR

The PEDESTRIAN dataset provides a comprehensive collection of egocentric videos capturing urban sidewalk obstacles, enabling the development of real-time obstacle detection systems to improve pedestrian safety.

Contribution

This work introduces a new egocentric dataset with 340 videos of 29 obstacle types, facilitating research in pedestrian obstacle detection.

Findings

Deep learning models trained on the dataset achieved promising detection accuracy.

The dataset serves as a benchmark for future obstacle recognition research.

Results demonstrate the potential for real-time pedestrian safety applications.

Abstract

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.