Which Way Are You Going? Imitative Decision Learning for Path Forecasting in Dynamic Scenes

Path forecasting is a pivotal step toward understanding dynamic scenes and an emerging topic in the computer vi- sion field. This task is challenging due to the multimodal nature of the future, namely, given a partial history, there is more than one plausible prediction. Yet, the state-of-the-art methods seem not fully responsive to this innate variabil- ity. Hence, how to better foresee the forthcoming trajectory in dynamic scenes has to be more thoroughly pursued. To this end, we propose a novel Imitative Decision Learning (IDL) approach. It delves deeper into the key that inher- ently characterizes the multimodality - the latent decision. The proposed IDL first infers the distribution of such latent decisions by learning from moving histories. A policy is then generated by taking the sampled latent decision into account to predict the future. Different plausible upcoming paths corresponds to each sampled latent decision. This ap- proach significantly differs from the mainstream literature that relies on a predefined latent variable to extrapolate di- verse predictions. In order to augment the understanding of the latent decision and resultant mutimodal future, we in- vestigate their connection through mutual information op- timization. Moreover, the proposed IDL integrates spatial and temporal dependencies into one single framework, in contrast to handling them with two-step settings. As a re- sult, our approach enables simultaneous anticipation of the paths of all pedestrians in the scene. We assess our pro- posal on the large-scale SAP, ETH and UCY datasets. The experiments show that IDL introduces considerable margin improvements with respect to recent leading studies.