Compact UAV systems, while advancing delivery and surveillance, pose significant security challenges due to their small size, which hinders detection by traditional methods. This paper presents a cost-effective, unsupervised UAV detection method using spatial-temporal sequence processing to fuse multiple LiDAR scans for accurate UAV tracking in real-world scenarios. Our approach segments point clouds into foreground and background, analyzes spatial-temporal data, and employs a scoring mechanism to enhance detection accuracy. Tested on a public dataset, our solution placed 4th in the CVPR 2024 UG2+ Challenge, demonstrating its practical effectiveness. We plan to open-source all designs, code, and sample data for the research community github.com/lianghanfang/UnLiDAR-UAV-Est.
@article{arxiv.2412.12716,
title = {Unsupervised UAV 3D Trajectories Estimation with Sparse Point Clouds},
author = {Hanfang Liang and Yizhuo Yang and Jinming Hu and Jianfei Yang and Fen Liu and Shenghai Yuan},
journal= {arXiv preprint arXiv:2412.12716},
year = {2025}
}
Comments
This paper has been accepted for presentation at the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2025. 2025 IEEE Trademark. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses