  Object counting is an important task in computer vision due to its growing
demand in applications such as traffic monitoring or surveillance. In this
paper, we consider object counting as a learning problem of a joint feature
extraction and pixel-wise object density estimation with
Convolutional-Deconvolutional networks. We introduce a novel counting model,
named Gated U-Net (GU-Net). Specifically, we propose to enrich the U-Net
architecture with the concept of learnable short-cut connections. Standard
short-cut connections are connections between layers in deep neural networks
which skip at least one intermediate layer. Instead of simply setting short-cut
connections, we propose to learn these connections from data. Therefore, our
short-cuts can work as gating units, which optimize the flow of information
between convolutional and deconvolutional layers in the U-Net architecture. We
evaluate the introduced GU-Net architecture on three commonly used benchmark
data sets for object counting. GU-Nets consistently outperform the base U-Net
architecture, and achieve state-of-the-art performance.
