Enhancing and Dissecting Crowd Counting By Synthetic Data

TL;DR

This paper introduces a large synthetic crowd dataset, CrowdX, to improve crowd counting models and analyze how various factors affect their performance, leading to significant accuracy gains and deeper understanding.

Contribution

The creation of the CrowdX synthetic dataset and its use to enhance crowd counting models and analyze influencing factors is a novel approach.

Findings

Performance of ESA-Net improved by 8.4% using CrowdX.

Pre-trained MCNN and CSRNet also show significant improvements.

Analysis of factors like background, camera angle, and density impacts.

Abstract

In this article, we propose a simulated crowd counting dataset CrowdX, which has a large scale, accurate labeling, parameterized realization, and high fidelity. The experimental results of using this dataset as data enhancement show that the performance of the proposed streamlined and efficient benchmark network ESA-Net can be improved by 8.4\%. The other two classic heterogeneous architectures MCNN and CSRNet pre-trained on CrowdX also show significant performance improvements. Considering many influencing factors determine performance, such as background, camera angle, human density, and resolution. Although these factors are important, there is still a lack of research on how they affect crowd counting. Thanks to the CrowdX dataset with rich annotation information, we conduct a large number of data-driven comparative experiments to analyze these factors. Our research provides a reference for a deeper understanding of the crowd counting problem and puts forward some useful suggestions in the actual deployment of the algorithm.