Traffic light detection and intersection crossing using mobile computer vision

Lynne Grewei; Christopher Lagali

doi:10.1117/12.2264552

2 May 2017 Traffic light detection and intersection crossing using mobile computer vision

Lynne Grewei, Christopher Lagali

Proceedings Volume 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI; 1020012 (2017) https://doi.org/10.1117/12.2264552
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

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.

Conference Presentation

Citation Download Citation

Lynne Grewei and Christopher Lagali "Traffic light detection and intersection crossing using mobile computer vision", Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 1020012 (2 May 2017); https://doi.org/10.1117/12.2264552

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