publication . Conference object . Preprint . 2018

Learning Short-Cut Connections for Object Counting

Onoro-Rubio, D.; Niepert, M.; Roberto Lopez-Sastre;
Open Access
  • Published: 15 Nov 2018
  • Publisher: Zenodo
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.
free text keywords: Computer Science - Computer Vision and Pattern Recognition
Funded by
  • Funder: European Commission (EC)
  • Project Code: 761508
  • Funding stream: H2020 | IA
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Open Access
Conference object . 2018
Providers: Datacite
Open Access
Conference object . 2018
Providers: ZENODO
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