  Egocentric, or first-person vision which became popular in recent years with
an emerge in wearable technology, is different than exocentric (third-person)
vision in some distinguishable ways, one of which being that the camera wearer
is generally not visible in the video frames. Recent work has been done on
action and object recognition in egocentric videos, as well as work on
biometric extraction from first-person videos. Height estimation can be a
useful feature for both soft-biometrics and object tracking. Here, we propose a
method of estimating the height of an egocentric camera without any calibration
or reference points. We used both traditional computer vision approaches and
deep learning in order to determine the visual cues that results in best height
estimation. Here, we introduce a framework inspired by two stream networks
comprising of two Convolutional Neural Networks, one based on spatial
information, and one based on information given by optical flow in a frame.
Given an egocentric video as an input to the framework, our model yields a
height estimate as an output. We also incorporate late fusion to learn a
combination of temporal and spatial cues. Comparing our model with other
methods we used as baselines, we achieve height estimates for videos with a
Mean Average Error of 14.04 cm over a range of 103 cm of data, and
classification accuracy for relative height (tall, medium or short) up to
93.75% where chance level is 33%.
