Image2Height: Self-height Estimation from a Single-Shot Image

This paper analyzes a self-height estimation method from a single-shot image using a convolutional architecture. To estimate the height where the image was captured, the method utilizes object-related scene structure contained in a single image in contrast to SLAM methods, which use geometric calculation on sequential images. Therefore, a variety of application domains from wearable computing (e.g., estimation of wearer’s height) to the analysis of archived images can be considered. This paper shows that (1) fine tuning from a pretrained object-recognition architecture contributes also to self-height estimation and that (2) not only visual features but their location on an image is fundamental to the self-height estimation task. We verify these two points through the comparison of different learning conditions, such as preprocessing and initialization, and also visualization and sensitivity analysis using a dataset obtained in indoor environments.