Rémi Mégret

Human Daily Activities Indexing in Videos from Wearable Cameras for Monitoring of Patients with Dementia Diseases

Our research focuses on analysing human activities according to a known behaviorist scenario, in case of noisy and high dimensional collected data. The data come from the monitoring of patients with dementia diseases by wearable cameras. We define a structural model of video recordings based on a Hidden Markov Model. New spatio-temporal features, color features and localization features are proposed as observations. First results in recognition of activities are promising.

Recognition of Activities of Daily Living in natural “at home” scenario for assessment of Alzheimer's disease patients

In this paper we tackle the problem of Instrumental Activities of Daily Living (IADLs) recognition from wearable videos in a Home Clinical scenario. The aim of this research is to provide an accessible and yet detailed video-based navigation interface of patients with dementia/Alzheimer disease to doctors and caregivers. A joint work between a memory clinic and computer vision scientists enabled studying real-case life scenarios of a dyad couple consisting of a caregiver and patient with Alzheimer. As a result of this collaboration, a new @Home, real-life video dataset was recorded, from which a truly relevant taxonomy of activities was extracted. Following a state of the art Activity Recognition framework we further studied and assessed these IADLs in term of recognition performances with different calibration approaches.

Spatial and multi-resolution context in visual indexing

Recent trends in visual indexing make appear a large family of methods which use a local image representation via descriptors associated to the interest points, see chapter 2. Such approaches mostly ”forget” any structure in the image considering unordered sets of descriptors or their histograms as image model. Hence, more advanced approaches try to overcome this drawback by adding spatial arrangements to the interest points. In this chapter we will present two trends in incorporation of spatial context into visual description, such as considering spatial context in the process of matching of signatures on one hand and design of structural descriptors which are then used in a global Bag-of-Visual-Words (BoVW) approach on the other hand. As images and video are mainly available in a compressed form, we shortly review global descriptors extracted from compressed stream and hence less sensible to compression artifacts. Furthermore, on the basis of scalable, multiresolution/ multi-scale visual content representation in modern compression standards, we study how this multi-resolution context can be efficiently incorporated into a BoVW approach.

Background mosaic from egomotion

A framework is presented that produces the mosaic corresponding to the background object of an image sequence. It is based on the dominant motion assumption, which states that the background has a parametric motion and occupies the main part of the images. The foreground objects are localised by their different motion. This localisation is computed together with the background motion in an iterative method. The regions corresponding to the background are then pasted onto the mosaic using classic methods adapted to object elimination or a new mosaicking method based on a striping that takes the foreground objects localisation into account.