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Mixture of Counting CNNs: Adaptive Integration of CNNs Specialized to Specific Appearance for Crowd Counting

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arxiv 1703.09393 v1 pith:4ERPW5CO submitted 2017-03-28 cs.CV

Shohei Kumagai , Kazuhiro Hotta , Takio Kurita This is my paper · ORCID
classification cs.CV
keywords appearancecnnscountingcrowdchangeslargemethodpredictor
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This paper proposes a crowd counting method. Crowd counting is difficult because of large appearance changes of a target which caused by density and scale changes. Conventional crowd counting methods generally utilize one predictor (e,g., regression and multi-class classifier). However, such only one predictor can not count targets with large appearance changes well. In this paper, we propose to predict the number of targets using multiple CNNs specialized to a specific appearance, and those CNNs are adaptively selected according to the appearance of a test image. By integrating the selected CNNs, the proposed method has the robustness to large appearance changes. In experiments, we confirm that the proposed method can count crowd with lower counting error than a CNN and integration of CNNs with fixed weights. Moreover, we confirm that each predictor automatically specialized to a specific appearance.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Count2Density: Crowd Density Estimation without Location-level Annotations

    cs.CV 2025-09 conditional novelty 6.0 of 10

    Count2Density maps per-image crowd counts into spatial density maps via an EMA historical bank and hypergeometric sampling, eliminating the need for point annotations.

  2. Attend To Count: Crowd Counting with Adaptive Capacity Multi-scale CNNs

    cs.CV 2019-08 conditional novelty 4.0 of 10

    A three-part CNN with a count attention mechanism routes dense and sparse image regions to networks of different capacities and reports state-of-the-art counting errors on five benchmarks.

  3. Crowd Scene Analysis using Deep Learning Techniques

    cs.CV 2025-05 reject novelty 3.0 of 10

    The paper describes a 5-column M-CNN with rotation self-supervision and Sinkhorn distribution matching for crowd counting and a VGG19-LSTM with dense residual blocks for violence detection, but the evidence does not s...

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