Stacked Pooling: Improving Crowd Counting by Boosting Scale Invariance

TL;DR

This paper introduces stacked pooling, a simple yet effective method to enhance scale invariance in CNNs for crowd counting, leading to improved accuracy without extra parameters.

Contribution

It proposes stacked pooling as a computationally efficient alternative to multi-kernel pooling to boost scale invariance in crowd counting models.

Findings

Stacked pooling outperforms vanilla pooling in most benchmark tests.

The method improves crowd density estimation accuracy.

No additional parameters are introduced by the pooling modules.

Abstract

In this work, we explore the cross-scale similarity in crowd counting scenario, in which the regions of different scales often exhibit high visual similarity. This feature is universal both within an image and across different images, indicating the importance of scale invariance of a crowd counting model. Motivated by this, in this paper we propose simple but effective variants of pooling module, i.e., multi-kernel pooling and stacked pooling, to boost the scale invariance of convolutional neural networks (CNNs), benefiting much the crowd density estimation and counting. Specifically, the multi-kernel pooling comprises of pooling kernels with multiple receptive fields to capture the responses at multi-scale local ranges. The stacked pooling is an equivalent form of multi-kernel pooling, while, it reduces considerable computing cost. Our proposed pooling modules do not introduce extra parameters into model and can easily take place of the vanilla pooling layer in implementation. In empirical study on two benchmark crowd counting datasets, the stacked pooling beats the vanilla pooling layer in most cases.