Guardian Angel: A Novel Walking Aid for the Visually Impaired

This work introduces Guardian Angel, an Android App that assists visually impaired people to avoid danger in complex traffic environment. The system, consisting of object detection by pretrained YOLO model, distance estimation and moving direction estimation, provides information about surrounding vehicles and alarms users of potential danger without expensive special purpose device. With an experiment of 8 subjects, we corroborate that in terms of satisfaction score in pedestrian-crossing experiment with the assistance of our App using a smartphone is better than when without under 99% confidence level. The time needed to cross a road is shorter on average with the assistance of our system, however, not reaching significant difference by our experiment. The App has been released in Google Play Store, open to the public for free.

Prediction Model for Semitransparent Watercolor Pigment Mixtures Using Deep Learning with a Dataset of Transmittance and Reflectance

Learning color mixing is difficult for novice painters. In order to support novice painters in learning color mixing, we propose a prediction model for semitransparent pigment mixtures and use its prediction results to create a Smart Palette system. Such a system is constructed by first building a watercolor dataset with two types of color mixing data, indicated by transmittance and reflectance: incrementation of the same primary pigment and a mixture of two different pigments. Next, we apply the collected data to a deep neural network to train a model for predicting the results of semitransparent pigment mixtures. Finally, we constructed a Smart Palette that provides easily-followable instructions on mixing a target color with two primary pigments in real life: when users pick a pixel, an RGB color, from an image, the system returns its mixing recipe which indicates the two primary pigments being used and their quantities.