On the Robustness and Real-time Adaptation of UAV-based Crowd Localization in Complex and High-density Scenes

This paper presents a novel approach for robust and real-time crowd localization using unmanned aerial vehicles (UAVs) in complex scenarios. The approach follows the single-stage detection paradigm, aiming to balance time and space complexity efficiently. To achieve this, the proposed method leverages pertaining of the architecture with UAV-oriented object detection datasets, specifically VisDrone and UAVDT. Additionally, selective data augmentations are applied to adapt the model to the crowd localization vision task in the UAV-oriented theme, using datasets such as NWPU-Crowd, UCF-QNRF, and Shanghai Tech. The authors conducted ablation studies to assess the contributions of pretraining and different adaptation techniques to the overall performance. The results demonstrate that the proposed approach achieves high performance on the edge, specifically on the NVIDIA Jetson Xavier NX board, with well-balanced options that align with the application design requirements. This enables effective crowd localization in complex scenarios using UAVs. Furthermore, the study sheds light on the lack of existing datasets specifically designed for UAV-based crowd localization and proposes potential solutions to address this gap. By presenting both quantitative and qualitative results, the paper shows the effectiveness of the proposed approach, achieving satisfactory performance while reducing inference time and the number of parameters. This work has good potential to significantly contribute to the robotics and automation society by introducing the first approach for UAV-based crowd localization on the edge platform. It also provides valuable insights into the importance of pretraining and data adaptations techniques in this context. Overall, the proposed approach offers a promising solution for crowd localization in complex scenarios using UAVs, and it has the potential to advance various applications in real-world scenarios.

I. Introduction

Crowd localization is an essential task in many real-world applications, where it involves identifying the number of individuals in a particular area and their spatial locations [1]. With the increasing availability of UAVs equipped with high-resolution cameras and advanced processing capabilities, UAV-based crowd localization has become necessary for the scenarios of surveillance, emergency response, disaster management, and large-scale event monitoring [2], [3].