  Although First Person Vision systems can sense the environment from the
user's perspective, they are generally unable to predict his intentions and
goals. Since human activities can be decomposed in terms of atomic actions and
interactions with objects, intelligent wearable systems would benefit from the
ability to anticipate user-object interactions. Even if this task is not
trivial, the First Person Vision paradigm can provide important cues to address
this challenge. We propose to exploit the dynamics of the scene to recognize
next-active-objects before an object interaction begins. We train a classifier
to discriminate trajectories leading to an object activation from all others
and forecast next-active-objects by analyzing fixed-length trajectory segments
within a temporal sliding window. The proposed method compares favorably with
respect to several baselines on the Activity of Daily Living (ADL) egocentric
dataset comprising 10 hours of videos acquired by 20 subjects while performing
unconstrained interactions with several objects.
