Gradient Profiling for Pedestrian Services

Jain, Shubham

doi:10.1145/2752746.2752784

Cited by 2 publications

(1 citation statement)

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“…LookUp leverages these roadway features to develop a sensing system that can automatically detect transitions from a sidewalk into the road [44], [45]. Importantly, it can track the inclination of the ground and detect the sloped transitions (ramps) that are installed at many dedicated crossing to improve accessibility.…”

Section: B Gradient Profiling For Pedestrian Safetymentioning

confidence: 99%

Recognizing Textures with Mobile Cameras for Pedestrian Safety Applications

Jain

Gruteser

2019

IEEE Trans. on Mobile Comput.

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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.

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Section: B Gradient Profiling For Pedestrian Safetymentioning

confidence: 99%