  Compared with single image based crowd counting, video provides the
spatial-temporal information of the crowd that would help improve the
robustness of crowd counting. But translation, rotation and scaling of people
lead to the change of density map of heads between neighbouring frames.
Meanwhile, people walking in/out or being occluded in dynamic scenes leads to
the change of head counts. To alleviate these issues in video crowd counting, a
Locality-constrained Spatial Transformer Network (LSTN) is proposed.
Specifically, we first leverage a Convolutional Neural Networks to estimate the
density map for each frame. Then to relate the density maps between
neighbouring frames, a Locality-constrained Spatial Transformer (LST) module is
introduced to estimate the density map of next frame with that of current
frame. To facilitate the performance evaluation, a large-scale video crowd
counting dataset is collected, which contains 15K frames with about 394K
annotated heads captured from 13 different scenes. As far as we know, it is the
largest video crowd counting dataset. Extensive experiments on our dataset and
other crowd counting datasets validate the effectiveness of our LSTN for crowd
counting.
