Modeling motion patterns of dynamic objects by IOHMM

This paper presents a novel approach to model motion patterns of dynamic objects, such as people and vehicles, in the environment with the occupancy grid map representation. Corresponding to the ever-changing nature of the motion pattern of dynamic objects, we model each occupancy grid cell by an IOHMM, which is an inhomogeneous variant of the HMM. This distinguishes our work from existing methods which use the conventional HMM, assuming motion evolving according to a stationary process. By introducing observations of neighbor cells in the previous time step as input of IOHMM, the transition probabilities in our model are dependent on the occurrence of events in the cell's neighborhood. This enables our method to model the spatial correlation of dynamics across cells. A sequence processing example is used to illustrate the advantage of our model over conventional HMM based methods. Results from the experiments in an office corridor environment demonstrate that our method is capable of capturing dynamics of such human living environments.