用稀疏点云无监督追踪无人机轨迹，提升小尺寸无人机检测效果
Unsupervised UAV 3D Trajectories Estimation with Sparse Point Clouds
- 通过时空序列融合多帧激光雷达数据，分割前景背景并评分增强检测
- 在CVPR 2024 UG2+挑战赛中位列第4，验证了真实场景有效性
- 适合关注低成本无人机检测与无监督点云处理的研究者
紧凑型无人机在配送和监控中广泛应用，但因其体积小难以被传统方法探测，带来显著安全挑战。本文提出一种低成本、无监督的无人机检测方法，利用时空序列处理融合多帧激光雷达扫描，实现真实场景下的精准追踪。方法首先将点云分割为前景与背景，分析时空特征，并引入评分机制提升检测准确率。在公开数据集上的测试中，该方案在CVPR 2024 UG2+挑战赛中排名第四，证明了其实际有效性。项目代码、设计与样例数据将开源至github.com/lianghanfang/UnLiDAR-UAV-Est，供研究社区使用。
原文摘要 · Abstract (English)
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.
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