  Crowd counting aims to count the number of instantaneous people in a crowded
space, and many promising solutions have been proposed for single image crowd
counting. With the ubiquitous video capture devices in public safety field, how
to effectively apply the crowd counting technique to video content has become
an urgent problem. In this paper, we introduce a novel framework based on
temporal aware modeling of the relationship between video frames. The proposed
network contains a few dilated residual blocks, and each of them consists of
the layers that compute the temporal convolutions of features from the adjacent
frames to improve the prediction. To alleviate the expensive computation and
satisfy the demand of fast video crowd counting, we also introduce a
lightweight network to balance the computational cost with representation
ability. We conduct experiments on the crowd counting benchmarks and
demonstrate its superiority in terms of effectiveness and efficiency over
previous video-based approaches.
