  We study video crowd counting, which is to estimate the number of objects
(people in this paper) in all the frames of a video sequence. Previous work on
crowd counting is mostly on still images. There has been little work on how to
properly extract and take advantage of the spatial-temporal correlation between
neighboring frames in both short and long ranges to achieve high estimation
accuracy for a video sequence. In this work, we propose Monet, a novel and
highly accurate motion-guided non-local spatial-temporal network for video
crowd counting. Monet first takes people flow (motion information) as guidance
to coarsely segment the regions of pixels where a person may be. Given these
regions, Monet then uses a non-local spatial-temporal network to extract
spatial-temporally both short and long-range contextual information. The whole
network is finally trained end-to-end with a fused loss to generate a
high-quality density map. Noting the scarcity and low quality (in terms of
resolution and scene diversity) of the publicly available video crowd datasets,
we have collected and built a large-scale video crowd counting datasets,
VidCrowd, to contribute to the community. VidCrowd contains 9,000 frames of
high resolution (2560 x 1440), with 1,150,239 head annotations captured in
different scenes, crowd density and lighting in two cities. We have conducted
extensive experiments on the challenging VideoCrowd and two public video crowd
counting datasets: UCSD and Mall. Our approach achieves substantially better
performance in terms of MAE and MSE as compared with other state-of-the-art
approaches.
