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.
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 (Presented at SPIE Defense + Security: April 12, 2017; Published: 2 May 2017); https://doi.org/10.1117/12.2264552.
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Study of self-shadowing effect as a simple means to realize nanostructured thin films and layers with special attentions to birefringent obliquely deposited thin films and photo-luminescent porous silicon