  We present a novel dataset and a novel algorithm for recognizing activities
of daily living (ADL) from a first-person wearable camera. Handled objects are
crucially important for egocentric ADL recognition. For specific examination of
objects related to users' actions separately from other objects in an
environment, many previous works have addressed the detection of handled
objects in images captured from head-mounted and chest-mounted cameras.
Nevertheless, detecting handled objects is not always easy because they tend to
appear small in images. They can be occluded by a user's body. As described
herein, we mount a camera on a user's wrist. A wrist-mounted camera can capture
handled objects at a large scale, and thus it enables us to skip object
detection process. To compare a wrist-mounted camera and a head-mounted camera,
we also develop a novel and publicly available dataset that includes videos and
annotations of daily activities captured simultaneously by both cameras.
Additionally, we propose a discriminative video representation that retains
spatial and temporal information after encoding frame descriptors extracted by
Convolutional Neural Networks (CNN).
