TY - JOUR
T1 - Recognizing Textures with Mobile Cameras for Pedestrian Safety Applications
AU - Jain, Shubham
AU - Gruteser, Marco
N1 - Funding Information:
The authors would like to thank all the volunteers who helped in collecting data for this project. This work was supported in part by the National Science Foundation (NSF) under Grant No CNS-1329939.
PY - 2019/8/1
Y1 - 2019/8/1
N2 - 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.
AB - 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.
KW - Pedestrian safety
KW - material classification
KW - mobile camera
KW - texture features
KW - urban sensing
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U2 - 10.1109/TMC.2018.2868659
DO - 10.1109/TMC.2018.2868659
M3 - Article
AN - SCOPUS:85052839856
VL - 18
SP - 1911
EP - 1923
JO - IEEE Transactions on Mobile Computing
JF - IEEE Transactions on Mobile Computing
SN - 1536-1233
IS - 8
M1 - 8454285
ER -