  On a minute-to-minute basis people undergo numerous fluid interactions with
objects that barely register on a conscious level. Recent neuroscientific
research demonstrates that humans have a fixed size prior for salient objects.
This suggests that a salient object in 3D undergoes a consistent transformation
such that people's visual system perceives it with an approximately fixed size.
This finding indicates that there exists a consistent egocentric object prior
that can be characterized by shape, size, depth, and location in the first
person view.
  In this paper, we develop an EgoObject Representation, which encodes these
characteristics by incorporating shape, location, size and depth features from
an egocentric RGBD image. We empirically show that this representation can
accurately characterize the egocentric object prior by testing it on an
egocentric RGBD dataset for three tasks: the 3D saliency detection, future
saliency prediction, and interaction classification. This representation is
evaluated on our new Egocentric RGBD Saliency dataset that includes various
activities such as cooking, dining, and shopping. By using our EgoObject
representation, we outperform previously proposed models for saliency detection
(relative 30% improvement for 3D saliency detection task) on our dataset.
Additionally, we demonstrate that this representation allows us to predict
future salient objects based on the gaze cue and classify people's interactions
with objects.
