Crowd counting via learning perspective for multi-scale multi-view Web images

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.

Keywords

crowd counting / Web images / perspective inference

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