  Forecasting human activities observed in videos is a long-standing challenge
in computer vision, which leads to various real-world applications such as
mobile robots, autonomous driving, and assistive systems. In this work, we
present a new visual forecasting task called crowd density forecasting. Given a
video of a crowd captured by a surveillance camera, our goal is to predict how
that crowd will move in future frames. To address this task, we have developed
the patch-based density forecasting network (PDFN), which enables forecasting
over a sequence of crowd density maps describing how crowded each location is
in each video frame. PDFN represents a crowd density map based on spatially
overlapping patches and learns density dynamics patch-wise in a compact latent
space. This enables us to model diverse and complex crowd density dynamics
efficiently, even when the input video involves a variable number of crowds
that each move independently. Experimental results with several public datasets
demonstrate the effectiveness of our approach compared with state-of-the-art
forecasting methods.
