Recognizing Textures with Mobile Cameras for Pedestrian Safety Applications

Shubham Jain, Marco Gruteser

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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 percent accuracy, and accurately identify when pedestrians transition from sidewalk to street.

Original languageEnglish (US)
Article number8454285
Pages (from-to)1911-1923
Number of pages13
JournalIEEE Transactions on Mobile Computing
Volume18
Issue number8
DOIs
StatePublished - Aug 1 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Keywords

  • Pedestrian safety
  • material classification
  • mobile camera
  • texture features
  • urban sensing

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