# A Mobile Robot Generating Video Summaries of Seniors' Indoor Activities

## TL;DR

This paper presents a system for generating concise video summaries of seniors' indoor activities captured by a mobile robot, using pose estimation, person identification, and action recognition to handle long, redundant, and challenging video data.

## Contribution

The novel approach integrates pose estimation, person identification, and action recognition to improve video summarization of indoor activities from a moving robot.

## Key findings

- Effective summarization of seniors' activities in challenging indoor videos
- Accurate detection and tracking of seniors using pose estimation and person ID
- Generation of diverse, representative keyframes for activity summaries

## Abstract

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.