  Modern methods for counting people in crowded scenes rely on deep networks to
estimate people densities in individual images. As such, only very few take
advantage of temporal consistency in video sequences, and those that do only
impose weak smoothness constraints across consecutive frames. In this paper, we
advocate estimating people flows across image locations between consecutive
images and inferring the people densities from these flows instead of directly
regressing them. This enables us to impose much stronger constraints encoding
the conservation of the number of people. As a result, it significantly boosts
performance without requiring a more complex architecture. Furthermore, it
allows us to exploit the correlation between people flow and optical flow to
further improve the results. We also show that leveraging people conservation
constraints in both a spatial and temporal manner makes it possible to train a
deep crowd counting model in an active learning setting with much fewer
annotations. This significantly reduces the annotation cost while still leading
to similar performance to the full supervision case.
