无需标注数据，通过时空聚类分离无人机点云，有效应对复杂背景干扰。
Separating Drone Point Clouds From Complex Backgrounds by Cluster Filter -- Technical Report for CVPR 2024 UG2 Challenge
- 基于时空序列的无监督聚类，融合多帧激光雷达数据追踪无人机
- 在MMAUD数据集上取得第4名，实现高精度动态目标检测
- 适合反无人机系统开发人员及点云处理研究者参考
小型无人机在冲突与扰动场景中的日益普及加剧了其威胁，亟需有效的反制手段。然而，无人机体积小，传统依赖标注的点云或图像检测方法难以有效识别。本文提出一种无监督无人机检测方法，通过时空序列处理融合多帧激光雷达数据，实现对无人机的定位与跟踪。利用全局-局部序列聚类器进行前后景分割，从时空密度和时空体素两个维度解析点云数据。同时设计点云运动目标评分机制，结合时序检测提升准确率与效率。在MMAUD数据集上的测试中，本方法在CVPR 2024 UG2+挑战赛中位列第4，验证了其在实际场景中的有效性。
原文摘要 · Abstract (English)
The increasing deployment of small drones as tools of conflict and disruption has amplified their threat, highlighting the urgent need for effective anti-drone measures. However, the compact size of most drones presents a significant challenge, as traditional supervised point cloud or image-based object detection methods often fail to identify such small objects effectively. This paper proposes a simple UAV detection method using an unsupervised pipeline. It uses spatial-temporal sequence processing to fuse multiple lidar datasets effectively, tracking and determining the position of UAVs, so as to detect and track UAVs in challenging environments. Our method performs front and rear background segmentation of point clouds through a global-local sequence clusterer and parses point cloud data from both the spatial-temporal density and spatial-temporal voxels of the point cloud. Furthermore, a scoring mechanism for point cloud moving targets is proposed, using time series detection to improve accuracy and efficiency. We used the MMAUD dataset, and our method achieved 4th place in the CVPR 2024 UG2+ Challenge, confirming the effectiveness of our method in practical applications.
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