# Crowd Counting with Decomposed Uncertainty

## TL;DR

This paper introduces a scalable neural network framework for crowd counting that quantifies decomposed uncertainty using bootstrap ensembles, enhancing prediction insights and achieving state-of-the-art results on benchmark datasets.

## Contribution

The work presents a novel uncertainty quantification method for crowd counting using bootstrap ensembles, improving decision-making and prediction accuracy.

## Key findings

- Provides additional insight into crowd counting uncertainty
- Achieves state-of-the-art performance on benchmark datasets
- Simple to implement and scalable approach

## Abstract

Research in neural networks in the field of computer vision has achieved remarkable accuracy for point estimation. However, the uncertainty in the estimation is rarely addressed. Uncertainty quantification accompanied by point estimation can lead to a more informed decision, and even improve the prediction quality. In this work, we focus on uncertainty estimation in the domain of crowd counting. With increasing occurrences of heavily crowded events such as political rallies, protests, concerts, etc., automated crowd analysis is becoming an increasingly crucial task. The stakes can be very high in many of these real-world applications. We propose a scalable neural network framework with quantification of decomposed uncertainty using a bootstrap ensemble. We demonstrate that the proposed uncertainty quantification method provides additional insight to the crowd counting problem and is simple to implement. We also show that our proposed method exhibits the state of the art performances in many benchmark crowd counting datasets.