  This paper addresses the problem of anticipating the next-active-object
location in the future, for a given egocentric video clip where the contact
might happen, before any action takes place. The problem is considerably hard,
as we aim at estimating the position of such objects in a scenario where the
observed clip and the action segment are separated by the so-called ``time to
contact'' (TTC) segment. Many methods have been proposed to anticipate the
action of a person based on previous hand movements and interactions with the
surroundings. However, there have been no attempts to investigate the next
possible interactable object, and its future location with respect to the
first-person's motion and the field-of-view drift during the TTC window. We
define this as the task of Anticipating the Next ACTive Object (ANACTO). To
this end, we propose a transformer-based self-attention framework to identify
and locate the next-active-object in an egocentric clip.
  We benchmark our method on three datasets: EpicKitchens-100, EGTEA+ and
Ego4D. We also provide annotations for the first two datasets. Our approach
performs best compared to relevant baseline methods. We also conduct ablation
studies to understand the effectiveness of the proposed and baseline methods on
varying conditions. Code and ANACTO task annotations will be made available
upon paper acceptance.

Computer Vision and Pattern Recognition