Interlayer and intralayer scale aggregation for scale-invariant crowd counting

Mingjie Wang

doi:10.1016/j.neucom.2021.01.112

Interlayer and intralayer scale aggregation for scale-invariant crowd counting

2021-06-01

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DOI：10.1016/j.neucom.2021.01.112

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Crowd counting is an important vision task, which faces challenges on continuous scale variation within a given scene and huge density shift both within and across images. These challenges are typically addressed using multi-column structures in existing methods. However, such an approach does not provide consistent improvement and transferability due to limited ability in capturing multi-scale features, sensitivity to large density shift, and difficulty in training multi-branch models. To overcome these limitations, a Single-column Scalere-invariant Network (ScSiNet) is presented in this paper, which extracts sophisticated scale-invariant features via the combination of interlayer multi-scale integration and a novel intralayer scale-invariant transformation (SiT). Furthermore, in order to enlarge the diversity of densities, a randomly integrated loss is presented for training our single-branch method. Extensive experiments on public datasets demonstrate that the proposed method outperforms state-of-the-art approaches in counting accuracy and achieves remarkable transferability and scale-invariant property. (c) 2021 Elsevier B.V. All rights reserved.

Keywords:

Scale Invariance

Stochastic scale mixer

Transferability

Randomly integrated loss

Crowd counting

Interlayer and intralayer scale aggregation for scale-invariant crowd counting

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