A Mobile Robot Generating Video Summaries of Seniors' Indoor Activities
This addresses the problem of monitoring seniors' daily activities for remote family members, but it is incremental as it applies existing methods to a new robotic domain.
The paper tackles the problem of generating video summaries from seniors' indoor activities captured by a moving robot, addressing challenges like long sequences and redundant frames, and results in a system that uses pose estimation, person identification, and action recognition to produce summaries for remote family members.
We develop a system which generates summaries from seniors' indoor-activity videos captured by a social robot to help remote family members know their seniors' daily activities at home. Unlike the traditional video summarization datasets, indoor videos captured from a moving robot poses additional challenges, namely, (i) the video sequences are very long (ii) a significant number of video-frames contain no-subject or with subjects at ill-posed locations and scales (iii) most of the well-posed frames contain highly redundant information. To address this problem, we propose to \hl{exploit} pose estimation \hl{for detecting} people in frames\hl{. This guides the robot} to follow the user and capture effective videos. We use person identification to distinguish a target senior from other people. We \hl{also make use of} action recognition to analyze seniors' major activities at different moments, and develop a video summarization method to select diverse and representative keyframes as summaries.