Estimating Head Motion from Egocentric Vision.

The recent availability of lightweight, wearable cameras allows for collecting video data from a "first-person' perspective, capturing the visual world of the wearer in everyday interactive contexts. In this paper, we investigate how to exploit egocentric vision to infer multimodal behaviors from people wearing head-mounted cameras. More specifically, we estimate head (camera) motion from egocentric video, which can be further used to infer non-verbal behaviors such as head turns and nodding in multimodal interactions. We propose several approaches based on Convolutional Neural Networks (CNNs) that combine raw images and optical flow fields to learn to distinguish regions with optical flow caused by global ego-motion from those caused by other motion in a scene. Our results suggest that CNNs do not directly learn useful visual features with end-to-end training from raw images alone; instead, a better approach is to first extract optical flow explicitly and then train CNNs to integrate optical flow and visual information.