  Wearable technologies are enabling plenty of new applications of computer
vision, from life logging to health assistance. Many of them are required to
recognize the elements of interest in the scene captured by the camera. This
work studies the problem of object detection and localization on videos
captured by this type of camera. Wearable videos are a much more challenging
scenario for object detection than standard images or even another type of
videos, due to lower quality images (e.g. poor focus) or high clutter and
occlusion common in wearable recordings. Existing work typically focuses on
detecting the objects of focus or those being manipulated by the user wearing
the camera. We perform a more general evaluation of the task of object
detection in this type of video, because numerous applications, such as
marketing studies, also need detecting objects which are not in focus by the
user. This work presents a thorough study of the well known YOLO architecture,
that offers an excellent trade-off between accuracy and speed, for the
particular case of object detection in wearable video. We focus our study on
the public ADL Dataset, but we also use additional public data for
complementary evaluations. We run an exhaustive set of experiments with
different variations of the original architecture and its training strategy.
Our experiments drive to several conclusions about the most promising
directions for our goal and point us to further research steps to improve
detection in wearable videos.
