Deep Neural Network for Estimating Value of Quality of Life in Driving Scenes.

The purpose of this research is to estimate a value of Quality of Life (QoL) of an image in a driving scene from only the image. The system suggesting optimal transportation methods and routes from a current place to a destination has been developed. The QoL value is used for the system. A method to estimate the QoL value easily is needed. This paper proposes a method for estimating the QoL value of the image. The image is segmented by a semantic segmentation method based on the Deep Neural Network (DNN). The rates of the total amount of the object region of each object class to the whole image region are calculated. The rates are used as indicators for estimating the QoL value. The MultiLayer Perceptron (MLP) learns the relationship between the QoL value and the rates. The DNN for estimating the QoL value from only the input image is constructed by connecting the DNN based semantic segmentation model and the MLP. The effectiveness of the proposed method is demonstrated by the experiments.