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Traffic light detection and intersection crossing using mobile computer vision
Author(s): Lynne Grewei; Christopher Lagali
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Paper Abstract

The solution for Intersection Detection and Crossing to support the development of blindBike an assisted biking system for the visually impaired is discussed. Traffic light detection and intersection crossing are key needs in the task of biking. These problems are tackled through the use of mobile computer vision, in the form of a mobile application on an Android phone. This research builds on previous Traffic Light detection algorithms with a focus on efficiency and compatibility on a resource-limited platform. Light detection is achieved through blob detection algorithms utilizing training data to detect patterns of Red, Green and Yellow in complex real world scenarios where multiple lights may be present. Also, issues of obscurity and scale are addressed. Safe Intersection crossing in blindBike is also discussed. This module takes a conservative “assistive” technology approach. To achieve this blindBike use’s not only the Android device but, an external bike cadence Bluetooth/Ant enabled sensor. Real world testing results are given and future work is discussed.

Paper Details

Date Published: 2 May 2017
PDF: 11 pages
Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 1020012 (2 May 2017); doi: 10.1117/12.2264552
Show Author Affiliations
Lynne Grewei, California State Univ., East Bay (United States)
Christopher Lagali, Calfornia State Univ., East Bay (United States)


Published in SPIE Proceedings Vol. 10200:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI
Ivan Kadar, Editor(s)

Video Presentation

Traffic-light-detection-and-intersection-crossing-using-mobile-computer-vision



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