  Automatic analysis of highly crowded people has attracted extensive attention
from computer vision research. Previous approaches for crowd counting have
already achieved promising performance across various benchmarks. However, to
deal with the real situation, we hope the model run as fast as possible while
keeping accuracy. In this paper, we propose a compact convolutional neural
network for crowd counting which learns a more efficient model with a small
number of parameters. With three parallel filters executing the convolutional
operation on the input image simultaneously at the front of the network, our
model could achieve nearly real-time speed and save more computing resources.
Experiments on two benchmarks show that our proposed method not only takes a
balance between performance and efficiency which is more suitable for actual
scenes but also is superior to existing light-weight models in speed.
