Novel planning-based algorithms for human motion prediction

Human motion prediction from visual tracking is a challenging problem with a wide array of applications such as robotics, video surveillance and situation understanding. Recently, planning-based methods -which assume that people move by planning over a cost function- have emerged as one of the most promising alternatives. Nevertheless, state of the art planning based algorithms have shortcomings regarding their computational complexity and ability to predict for arbitrary time intervals. This paper addresses these shortcomings by leveraging alternative planning techniques (Fast Marching Method) and formulating efficient algorithms for goal estimation and full spatiotemporal prediction with lower complexity than comparable approaches. In preliminary experiments, the proposed method significantly outperforms the accuracy of the current state-of-the-art approach while reducing the computation time by a factor of 30 using a parallel version of our algorithm.