  Egocentric vision is an emerging field of computer vision that is
characterized by the acquisition of images and video from the first person
perspective. In this paper we address the challenge of egocentric human action
recognition by utilizing the presence and position of detected regions of
interest in the scene explicitly, without further use of visual features.
  Initially, we recognize that human hands are essential in the execution of
actions and focus on obtaining their movements as the principal cues that
define actions. We employ object detection and region tracking techniques to
locate hands and capture their movements. Prior knowledge about egocentric
views facilitates hand identification between left and right. With regard to
detection and tracking, we contribute a pipeline that successfully operates on
unseen egocentric videos to find the camera wearer's hands and associate them
through time. Moreover, we emphasize on the value of scene information for
action recognition. We acknowledge that the presence of objects is significant
for the execution of actions by humans and in general for the description of a
scene. To acquire this information, we utilize object detection for specific
classes that are relevant to the actions we want to recognize.
  Our experiments are targeted on videos of kitchen activities from the
Epic-Kitchens dataset. We model action recognition as a sequence learning
problem of the detected spatial positions in the frames. Our results show that
explicit hand and object detections with no other visual information can be
relied upon to classify hand-related human actions. Testing against methods
fully dependent on visual features, signals that for actions where hand motions
are conceptually important, a region-of-interest-based description of a video
contains equally expressive information with comparable classification
performance.
