  Capabilities of inference and prediction are significant components of visual
systems. In this paper, we address an important and challenging task of them:
visual path prediction. Its goal is to infer the future path for a visual
object in a static scene. This task is complicated as it needs high-level
semantic understandings of both the scenes and motion patterns underlying video
sequences. In practice, cluttered situations have also raised higher demands on
the effectiveness and robustness of the considered models. Motivated by these
observations, we propose a deep learning framework which simultaneously
performs deep feature learning for visual representation in conjunction with
spatio-temporal context modeling. After that, we propose a unified path
planning scheme to make accurate future path prediction based on the analytic
results of the context models. The highly effective visual representation and
deep context models ensure that our framework makes a deep semantic
understanding of the scene and motion pattern, consequently improving the
performance of the visual path prediction task. In order to comprehensively
evaluate the model's performance on the visual path prediction task, we
construct two large benchmark datasets from the adaptation of video tracking
datasets. The qualitative and quantitative experimental results show that our
approach outperforms the existing approaches and owns a better generalization
capability.
