  The estimation of crowd count in images has a wide range of applications such
as video surveillance, traffic monitoring, public safety and urban planning.
Recently, the convolutional neural network (CNN) based approaches have been
shown to be more effective in crowd counting than traditional methods that use
handcrafted features. However, the existing CNN-based methods still suffer from
large number of parameters and large storage space, which require high storage
and computing resources and thus limit the real-world application.
Consequently, we propose a deeply-recursive network (DR-ResNet) based on ResNet
blocks for crowd counting. The recursive structure makes the network deeper
while keeping the number of parameters unchanged, which enhances network
capability to capture statistical regularities in the context of the crowd.
Besides, we generate a new dataset from the video-monitoring data of Beijing
bus station. Experimental results have demonstrated that proposed method
outperforms most state-of-the-art methods with far less number of parameters.
