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 propose 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-cut can work as a gating unit, which optimizes the flow of information between convolutional and deconvolutional layers in the U-Net architecture. We evaluate the proposed 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.

本文提出了一种名为GU-Net的新型物体计数模型，采用卷积-反卷积网络进行特征提取和像素级物体密度估计，并使用可学习的 shortcut 建立门控连接以优化模型性能，在三个常用基准数据集上实现了当前领先的物体计数表现。

学习针对对象计数的快捷连接