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Crowd counting via learning perspective for multi-scale multi-view Web images |
Chong SHANG1, Haizhou AI1( ), Yi YANG2 |
1. Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
2. Huawei Technologies, Beijing 100084, China |
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Abstract Estimating the number of people in Web images still remains a challenging problem owing to the perspective variation, different views, and diverse backgrounds. Existing deep learning models still have difficulties in dealing with scenarios where the size of a person is either extremely large or extremely small. In this paper, we propose a novel perspective-aware architecture to estimate the number of people in a crowd in web images. Specifically,we use a two-stage framework, where we first learn a policy network to infer the perspective of the target scene, which outputs a scale label for the subsequent perspective normalization. Next, given the aligned inputs, we further adjust the scale-specific counting network to regress the final count. Experiments on challenging datasets demonstrate our approach can deal with a large perspective variation and that we have achieved state-of-theart results.
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| Keywords
crowd counting
Web images
perspective inference
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Corresponding Author(s):
Haizhou AI
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Just Accepted Date: 13 June 2017
Online First Date: 06 July 2018
Issue Date: 24 April 2019
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