  This paper presents a method for indexing activities of daily living in
videos obtained from wearable cameras. In the context of dementia diagnosis by
doctors, the videos are recorded at patients' houses and later visualized by
the medical practitioners. The videos may last up to two hours, therefore a
tool for an efficient navigation in terms of activities of interest is crucial
for the doctors. The specific recording mode provides video data which are
really difficult, being a single sequence shot where strong motion and sharp
lighting changes often appear. Our work introduces an automatic motion based
segmentation of the video and a video structuring approach in terms of
activities by a hierarchical two-level Hidden Markov Model. We define our
description space over motion and visual characteristics of video and audio
channels. Experiments on real data obtained from the recording at home of
several patients show the difficulty of the task and the promising results of
our approach.
