Recognizing Textures with Mobile Cameras for Pedestrian Safety Applications

TL;DR

This paper introduces TerraFirma, a novel method using smartphone cameras to recognize walking surface textures and predict street entry, enhancing pedestrian safety by detecting distracted pedestrians in urban environments.

Contribution

The paper presents a new texture recognition approach with a unique urban dataset, enabling smartphones to identify walking surfaces and alert pedestrians about street entry.

Findings

Smartphone cameras can distinguish paving materials with over 90% accuracy.

The system can detect when pedestrians transition from sidewalk to street.

The approach works across major cities like New York, Paris, and London.

Abstract

As smartphone rooted distractions become commonplace, the lack of compelling safety measures has led to a rise in the number of injuries to distracted walkers. Various solutions address this problem by sensing a pedestrian's walking environment. Existing camera-based approaches have been largely limited to obstacle detection and other forms of object detection. Instead, we present TerraFirma, an approach that performs material recognition on the pedestrian's walking surface. We explore, first, how well commercial off-the-shelf smartphone cameras can learn texture to distinguish among paving materials in uncontrolled outdoor urban settings. Second, we aim at identifying when a distracted user is about to enter the street, which can be used to support safety functions such as warning the user to be cautious. To this end, we gather a unique dataset of street/sidewalk imagery from a pedestrian's perspective, that spans major cities like New York, Paris, and London. We demonstrate that modern phone cameras can be enabled to distinguish materials of walking surfaces in urban areas with more than 90% accuracy, and accurately identify when pedestrians transition from sidewalk to street.