ANTHROPOS-V: benchmarking the novel task of Crowd Volume Estimation

TL;DR

This paper introduces the first benchmark and dataset for Crowd Volume Estimation (CVE) using RGB images, enabling applications in safety, infrastructure, and weight estimation, with models surpassing baselines.

Contribution

It presents the ANTHROPOS-V synthetic dataset, defines CVE metrics, and proposes a novel methodology that outperforms existing baseline models.

Findings

Benchmark and dataset are publicly available.

Proposed models outperform baseline methods.

Synthetic data effectively transfers to real-world CVE tasks.

Abstract

We introduce the novel task of Crowd Volume Estimation (CVE), defined as the process of estimating the collective body volume of crowds using only RGB images. Besides event management and public safety, CVE can be instrumental in approximating body weight, unlocking weight sensitive applications such as infrastructure stress assessment, and assuring even weight balance. We propose the first benchmark for CVE, comprising ANTHROPOS-V, a synthetic photorealistic video dataset featuring crowds in diverse urban environments. Its annotations include each person's volume, SMPL shape parameters, and keypoints. Also, we explore metrics pertinent to CVE, define baseline models adapted from Human Mesh Recovery and Crowd Counting domains, and propose a CVE specific methodology that surpasses baselines. Although synthetic, the weights and heights of individuals are aligned with the real-world population distribution across genders, and they transfer to the downstream task of CVE from real images. Benchmark and code are available at github.com/colloroneluca/Crowd-Volume-Estimation.