Fully Convolutional Crowd Counting On Highly Congested Scenes

TL;DR

This paper introduces a fully convolutional crowd counting model that enhances accuracy in highly congested scenes by using data augmentation, a deep FCN architecture, and multi-scale inference, achieving state-of-the-art results.

Contribution

The paper presents a novel fully convolutional network with training augmentation and multi-scale inference for improved crowd counting in dense scenes.

Findings

Achieves state-of-the-art performance on Shanghaitech Part B and UCF CC 50 datasets.

Improves generalization and robustness in highly congested crowd scenes.

Analyzes images of any resolution or aspect ratio effectively.

Abstract

In this paper we advance the state-of-the-art for crowd counting in high density scenes by further exploring the idea of a fully convolutional crowd counting model introduced by (Zhang et al., 2016). Producing an accurate and robust crowd count estimator using computer vision techniques has attracted significant research interest in recent years. Applications for crowd counting systems exist in many diverse areas including city planning, retail, and of course general public safety. Developing a highly generalised counting model that can be deployed in any surveillance scenario with any camera perspective is the key objective for research in this area. Techniques developed in the past have generally performed poorly in highly congested scenes with several thousands of people in frame (Rodriguez et al., 2011). Our approach, influenced by the work of (Zhang et al., 2016), consists of the following contributions: (1) A training set augmentation scheme that minimises redundancy among training samples to improve model generalisation and overall counting performance; (2) a deep, single column, fully convolutional network (FCN) architecture; (3) a multi-scale averaging step during inference. The developed technique can analyse images of any resolution or aspect ratio and achieves state-of-the-art counting performance on the Shanghaitech Part B and UCF CC 50 datasets as well as competitive performance on Shanghaitech Part A.