LYTNet: A Convolutional Neural Network for Real-Time Pedestrian Traffic Lights and Zebra Crossing Recognition for the Visually Impaired-Reference-Cited by-同舟云学术

LYTNet: A Convolutional Neural Network for Real-Time Pedestrian Traffic Lights and Zebra Crossing Recognition for the Visually Impaired

Published:2019 Issue: Volume: Page:259-270
ISSN:0302-9743
Container-title:Computer Analysis of Images and Patterns
language:
Short-container-title:

Author:

Yu Samuel,Lee Heon,Kim John

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-29888-3_21

Reference19 articles.

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3. Barlow, J., Bentzen, B., Tabor, L.: Accessible Pedestrian Signals. National Cooperative Highway Research Program, Washington, D.C (2003)

4. Behrendt, K., Novak, L., Botros, R.: A deep learning approach to traffic lights: detection, tracking, and classification, pp. 1370–1377, May 2017. https://doi.org/10.1109/ICRA.2017.7989163

5. Blaauw, F., Krieke, L., Emerencia, A., Aiello, M., Jonge, P.: Personalized advice for enhancing well-being using automated impulse response analysis – AIRA, June 2017

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