Multi-scale Convolutional Neural Networks for Crowd Counting

TL;DR

This paper introduces a multi-scale convolutional neural network that improves crowd counting accuracy and efficiency by generating scale-relevant features within a single-column architecture, outperforming existing methods.

Contribution

The paper presents a novel multi-scale CNN architecture for crowd counting that simplifies the model while enhancing accuracy and robustness.

Findings

Outperforms state-of-the-art methods in accuracy

Uses fewer parameters for comparable or better performance

Demonstrates robustness across different crowd densities

Abstract

Crowd counting on static images is a challenging problem due to scale variations. Recently deep neural networks have been shown to be effective in this task. However, existing neural-networks-based methods often use the multi-column or multi-network model to extract the scale-relevant features, which is more complicated for optimization and computation wasting. To this end, we propose a novel multi-scale convolutional neural network (MSCNN) for single image crowd counting. Based on the multi-scale blobs, the network is able to generate scale-relevant features for higher crowd counting performances in a single-column architecture, which is both accuracy and cost effective for practical applications. Complemental results show that our method outperforms the state-of-the-art methods on both accuracy and robustness with far less number of parameters.