WSCF-MVCC: Weakly-supervised Calibration-free Multi-view Crowd Counting

TL;DR

This paper introduces WSCF-MVCC, a weakly-supervised, calibration-free multi-view crowd counting method that uses crowd counts directly for supervision, reducing annotation costs and improving scene-level counting accuracy.

Contribution

It proposes a novel weakly-supervised approach that eliminates the need for dense annotations and camera calibration, leveraging crowd counts and multi-scale priors for better multi-view counting.

Findings

Outperforms state-of-the-art methods on three datasets

Reduces annotation costs by using crowd counts directly

Achieves more accurate scene-level crowd estimation

Abstract

Multi-view crowd counting can effectively mitigate occlusion issues that commonly arise in single-image crowd counting. Existing deep-learning multi-view crowd counting methods project different camera view images onto a common space to obtain ground-plane density maps, requiring abundant and costly crowd annotations and camera calibrations. Hence, calibration-free methods are proposed that do not require camera calibrations and scene-level crowd annotations. However, existing calibration-free methods still require expensive image-level crowd annotations for training the single-view counting module. Thus, in this paper, we propose a weakly-supervised calibration-free multi-view crowd counting method (WSCF-MVCC), directly using crowd count as supervision for the single-view counting module rather than density maps constructed from crowd annotations. Instead, a self-supervised ranking loss that leverages multi-scale priors is utilized to enhance the model's perceptual ability without additional annotation costs. What's more, the proposed model leverages semantic information to achieve a more accurate view matching and, consequently, a more precise scene-level crowd count estimation. The proposed method outperforms the state-of-the-art methods on three widely used multi-view counting datasets under weakly supervised settings, indicating that it is more suitable for practical deployment compared with calibrated methods. Code is released in https://github.com/zqyq/Weakly-MVCC.