SPHERE Seminar Series: Scene understanding for Activity Monitoring
Dr. Francois Bremond and others
MVB 0.3, Merchant Venturers Building, Woodland Road
SPHERE seminar series will be hosting Dr. Francois Bremond Head of Inria-Nice Computer Vision team.
Since the population of the older persons grows highly, the improvement of the quality of life of older persons at home is of a great importance.
This can be achieved through the development of technologies for monitoring their activities at home. In this context, we propose
activity monitoring approaches which aim at analysing older person behaviors by combining heterogeneous sensor data to recognise critical
activities at home. In particular, this approach combines data provided by video cameras with data provided by environmental sensors attached
to house furnishings.
There are three categories of critical human activities:
- Activities which can be well described or modeled by users
- Activities which can be specified by users and that can be illustrated by positive/negative samples representative of the targeted activities
- Rare activities which are unknown to the users and which can be defined only with respect to frequent activities requiring large datasets
In this talk several techniques will be presented for the detection of people and for the recognition of human activities using in particular
2D or 3D video cameras combined with other sensors. More specifically, there are three categories of algorithms to recognise human activities:
Recognition engine using hand-crafted ontologies based on a priori knowledge (e.g. rules) predefined by users. This activity recognition engine is easily extendable and allows later integration of additional sensor information when needed [Robert 2012, Sacco 2013].
Supervised learning methods based on positive/negative samples representative of the targeted activities which have to be specified by users. These
methods are usually based on Bag-of-Words computing a large variety of spatio-temporal descriptors [Bilinski 2012, 2013].
Unsupervised (fully automated) learned methods based on clustering of frequent activity patterns on large datasets which can generate/discover new activity models [Pusiol 2012].
The talk will also discuss important issues related to Assisted Living and end-user benefits and illustrate the proposed activity monitoring approaches through several home care application datasets:
0117 331 5689