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

2025-02-06

AI Summary

To address the challenges of localizing and accurately detecting minute human targets in high-resolution UAV imagery, this paper proposes a novel point-guided, pixel-level crowd localization paradigm. Methodologically: (1) we design a Pixel Distill module to distill fine-grained spatial information; (2) we introduce UP-COUNT—the first large-scale benchmark dataset featuring realistic degradations including motion blur, camera shake, and target motion; and (3) we integrate multi-scale feature fusion with end-to-end differentiable localization modeling. Extensive experiments on UP-COUNT and DroneCrowd demonstrate significant improvements in average precision (AP) over state-of-the-art methods, achieving superior accuracy, real-time inference speed, and robustness against common aerial imaging distortions. The proposed framework enables reliable individual-level localization and counting in dynamic, dense urban crowds.

Technology Category

• Humans and AI: Crowd Sourcing and Human Computation
• Computer Vision: Segmentation
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• improve people localization in drone images
• enhance high-resolution image processing efficiency
• develop drone-specific dataset for crowd management

Innovation

• Point-oriented object localisation
• Pixel Distill module
• UP-COUNT dataset