Using HMMs for Discriminating Mobile from Static Objects in a 3D Occupancy Grid

This work is related to the development of a marker less system allowing the tracking of elderly people at home. Microsoft Kinect is a low cost 3D camera adapted to the tracking of human movements. We propose a method for making the fusion of the information provided by several Kinects. The observed space is tesselated into cells forming a 3D occupancy grid. We calculate a probability of occupation for each cell of the grid. From this probability we distinguish whether the cells are occupied or not by a static object (wall) or a mobile object (chair, human being). This categorization is realized in real-time using a simple three states HMM. The proposed method for discriminating between mobile and static objects in a room is the main contribution of this paper. The use of HMMs allows to deal with an aliasing problem since mobile objects result in the same observation as static objects. The approach is evaluated in simulation and in a real environment showing an efficient real-time discrimination between cells occupied by mobile objects and cells occupied by static objects.