Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images

TL;DR

This paper introduces a novel point-oriented localisation method and Pixel Distill module for high-resolution drone imagery, significantly improving accuracy and efficiency in crowd management applications.

Contribution

It presents a new localisation approach, a Pixel Distill module for high-res images, and a comprehensive dataset, advancing drone-based crowd localisation capabilities.

Findings

Outperforms existing methods on UP-COUNT and DroneCrowd datasets.

Enhances localisation accuracy in dynamic, real-world scenarios.

Demonstrates robustness against camera and object movement.

Abstract

Accurate people localisation using drones is crucial for effective crowd management, not only during massive events and public gatherings but also for monitoring daily urban crowd flow. Traditional methods for tiny object localisation using high-resolution drone imagery often face limitations in precision and efficiency, primarily due to constraints in image scaling and sliding window techniques. To address these challenges, a novel approach dedicated to point-oriented object localisation is proposed. Along with this approach, the Pixel Distill module is introduced to enhance the processing of high-definition images by extracting spatial information from individual pixels at once. Additionally, a new dataset named UP-COUNT, tailored to contemporary drone applications, is shared. It addresses a wide range of challenges in drone imagery, such as simultaneous camera and object movement during the image acquisition process, pushing forward the capabilities of crowd management applications. A comprehensive evaluation of the proposed method on the proposed dataset and the commonly used DroneCrowd dataset demonstrates the superiority of our approach over existing methods and highlights its efficacy in drone-based crowd object localisation tasks. These improvements markedly increase the algorithm's applicability to operate in real-world scenarios, enabling more reliable localisation and counting of individuals in dynamic environments.