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2020, Volume 21, Issue 11

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Frontiers of Information Technology & Electronic Engineering >> 2020, Volume 21, Issue 11 doi: 10.1631/FITEE.1900481

Aggregated context network for crowd counting

Affiliation(s): School of Computer Science and Technology, East China Normal University, Shanghai 200062, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; less

Received: 2019-09-08 Accepted: 2020-11-13 Available online: 2020-11-13
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Abstract

has been applied to a variety of applications such as video surveillance, traffic monitoring, assembly control, and other public safety applications. Context information, such as perspective distortion and background interference, is a crucial factor in achieving high performance for . While traditional methods focus merely on solving one specific factor, we aggregate sufficient context information into the network to tackle these problems simultaneously in this study. We build a fully convolutional network with two tasks, i.e., main density map estimation and auxiliary . The main task is to extract the multi-scale and spatial context information to learn the density map. The auxiliary task gives a comprehensive view of the background and foreground information, and the extracted information is finally incorporated into the main task by late fusion. We demonstrate that our network has better accuracy of estimation and higher robustness on three challenging datasets compared with state-of-the-art methods.

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