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

TL;DR

This paper introduces a crowd counting approach using multiple specialized CNNs that are adaptively selected based on appearance, improving accuracy over traditional single predictor methods.

Contribution

It presents a novel adaptive integration framework of multiple CNNs, each specialized for different appearances, to handle large scale and density variations in crowd counting.

Findings

Lower counting error compared to single CNNs.

Each CNN predictor specializes in a specific appearance.

Adaptive selection improves robustness to appearance changes.

Abstract

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.