Activity Recognition

The goal
The objective is to recognise an activity and to locate it in time. The CEA is applying this technology in the context of Smart Home, where the use cases are diverse. Indeed, the recognition of activities is useful for the assistance of elderly people at home. Thanks to this technology, it is possible to locate a person’s daily activities. The fact that he or she is no longer doing this or that activity as usual may indicate a loss of autonomy. Activity recognition is also used in a home automation context to improve the comfort of the inhabitants. This results in an adaptation of the light and sound environment to the person’s activity.

The database used
The CEA has created the DAHLIA (Daily Home Life Activity) dataset consisting of videos where people carry out the following seven activities: cooking, setting the table, eating, cleaning, washing up and working. The videos were recorded by three Kinect cameras in the kitchen area of the Mobile Mii platform. The DAHLIA dataset is public and can be downloaded at this address: www-mobilemii.cea.fr

The challenges
The main difficulty related to the recognition of activities is the great variability in carrying out the same activity. For example, “cooking” is composed of a multitude of sub-actions that can be performed in a different order from one person to another. It is also possible to confuse certain activities if the time window of observation is not large enough. It is also difficult to strictly delimit the beginning and end of an activity. Finally, we would like the technology developed to be independent from the camera’s point of view in order to be functional in any flat.

Proposed solution
To deal with this problem, the CEA has implemented the DOHT algorithm, which enables the activity of people in the scene to be recognised on the basis of their movements. The algorithm requires as input the estimation of the person’s 3D pose at each moment. The DOHT then estimates a confidence score for each class of activity according to the trajectories of the skeleton joints observed over a certain time window.
The recognition rate of the DOHT on the DAHLIA dataset is 70%.