Interlayer and Intralayer Scale Aggregation for Scale-invariant Crowd Counting

• URL: http://arxiv.org/abs/2005.11943v1
• Date: Mon, 25 May 2020 06:59:31 GMT
• Title: Interlayer and Intralayer Scale Aggregation for Scale-invariant Crowd Counting
• Authors: Mingjie Wang and Hao Cai and Jun Zhou and Minglun Gong
• Abstract summary: Single-column Scale-invariant Network (ScSiNet) is presented in this paper. It extracts sophisticated scale-invariant features via the combination of interlayer multi-scale integration and a novel intralayer scale-invariant transformation (SiT) Experiments on public datasets demonstrate that the proposed method consistently outperforms state-of-the-art approaches in counting accuracy and scale-invariant property.
• Score: 19.42355176075503
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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, sensitiveness to large density shift, and difficulty in training multi-branch models. To overcome these limitations, a Single-column Scale-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 consistently outperforms state-of-the-art approaches in counting accuracy and achieves remarkable transferability and scale-invariant property.

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