Human Trajectory Forecasting In Indoor Environments Using Geometric Context

摘要

A human trajectory is the likely path a human subject would take to get to a destination. Human trajectory forecasting algorithms try to estimate or predict this path. Such algorithms have wide applications in robotics, computer vision and video surveillance. Understanding the human behavior can provide useful information towards the design of these algorithms. Human trajectory forecasting algorithm is an interesting problem because the outcome is influenced by many factors, of which we believe that the geometry of the environment plays a significant role. In addressing this problem, we have built a model to estimate the occupancy behavior of humans based on the geometry and behavioral norms. We also develop a trajectory forecasting algorithm that understands this occupancy and leverages it for trajectory forecasting in previously unseen geometries. We perform experiments to quantify the error between our prediction model and the trajectories obtained from real world human subjects and compare them to state of the art models. Results obtained suggests a significant enhancement in the accuracy of trajectory forecasting by incorporating the occupancy behavior model.