Object counting is an important task in computer vision due to its growing demand. We introduce a novel counting model, named Gated U-Net (GU-Net) GU-Nets consistently outperform the base U- net architecture and achieve state-of-the-art performance.
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.