Modeling Spatial-Temporal Dynamics of Human Movements for Predicting Future Trajectories

This paper presents a novel approach to modeling the dynamics of human movements with a grid-based representation. For each grid cell, we formulate the local dynamics using a variant of the left-to-right HMM, and thus explicitly model the exiting direction from the current cell. The dependency of this process on the entry direction is captured by employing the InputOutput HMM (IOHMM). On a higher level, we introduce the place where the whole trajectory originated into the IOHMM framework forming a hierarchical input structure. Therefore, we manage to capture both local spatial-temporal correlations and the long-term dependency on faraway initiating events, thus enabling the developed model to incorporate more information and to generate more informative predictions of future trajectories. The experimental results in an office corridor environment verify the capabilities of our method.