Unsupervised UAV 3D Trajectories Estimation with Sparse Point Clouds

• URL: http://arxiv.org/abs/2412.12716v5
• Date: Sun, 19 Jan 2025 16:32:25 GMT
• Title: Unsupervised UAV 3D Trajectories Estimation with Sparse Point Clouds
• Authors: Hanfang Liang, Yizhuo Yang, Jinming Hu, Jianfei Yang, Fen Liu, Shenghai Yuan,
• Abstract summary: This paper presents a cost-effective, unsupervised UAV detection method using spatial-temporal sequence processing. 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.com/lianghanfang/UnLiDAR-UAV-Est.
• Score: 18.48877348628721
• License:
• Abstract: 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.

Related papers

• Separating Drone Point Clouds From Complex Backgrounds by Cluster Filter -- Technical Report for CVPR 2024 UG2 Challenge [3.4655579096694664]
  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. Our method achieved 4th place in the CVPR 2024 UG2+ Challenge, confirming the effectiveness of our method in practical applications.
  arXiv  Detail & Related papers  (2024-12-22T09:44:43Z)
• UAVDB: Trajectory-Guided Adaptable Bounding Boxes for UAV Detection [0.03464344220266879]
  This paper introduces UAVDB, a high-resolution UAV detection dataset constructed using Patch Intensity Convergence (PIC) We first validate the accuracy and efficiency of PIC-generated bounding boxes by comparing Intersection over Union (IoU) performance and runtime. We then benchmark UAVDB using state-of-the-art (SOTA) YOLO-series detectors, establishing UAVDB as a valuable resource for advancing long-range and high-resolution UAV detection.
  arXiv  Detail & Related papers  (2024-09-09T13:27:53Z)
• Clustering-based Learning for UAV Tracking and Pose Estimation [0.0]
  This work develops a clustering-based learning detection approach, CL-Det, for UAV tracking and pose estimation using two types of LiDARs. We first align the timestamps of Livox Avia data and LiDAR 360 data and then separate the point cloud of objects of interest (OOIs) from the environment. The proposed method shows competitive pose estimation performance and ranks 5th on the final leaderboard of the CVPR 2024 UG2+ Challenge.
  arXiv  Detail & Related papers  (2024-05-27T06:33:25Z)
• Multi-Modal UAV Detection, Classification and Tracking Algorithm -- Technical Report for CVPR 2024 UG2 Challenge [20.459377705070043]
  This report presents the 1st winning model for UG2+, a task in CVPR 2024 UAV Tracking and Pose-Estimation Challenge. We propose a multi-modal UAV detection, classification, and 3D tracking method for accurate UAV classification and tracking. Our system integrates cutting-edge classification techniques and sophisticated post-processing steps to boost accuracy and robustness.
  arXiv  Detail & Related papers  (2024-05-26T07:21:18Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images [96.66271207089096]
  FCOS-LiDAR is a fully convolutional one-stage 3D object detector for LiDAR point clouds of autonomous driving scenes. We show that an RV-based 3D detector with standard 2D convolutions alone can achieve comparable performance to state-of-the-art BEV-based detectors.
  arXiv  Detail & Related papers  (2022-05-27T05:42:16Z)
• Vision-based Anti-UAV Detection and Tracking [18.307952561941942]
  Unmanned aerial vehicles (UAV) have been widely used in various fields, and their invasion of security and privacy has aroused social concern. We propose a visible light mode dataset called Dalian University of Technology Anti-UAV dataset, DUT Anti-UAV. It contains a detection dataset with a total of 10,000 images and a tracking dataset with 20 videos that include short-term and long-term sequences.
  arXiv  Detail & Related papers  (2022-05-22T15:21:45Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach [93.67588414950656]
  In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) system in a 3D environment. We present a TD3-based trajectory design for completion time minimization (TD3-TDCTM) algorithm. Our simulation results show the superiority of the proposed TD3-TDCTM algorithm over three conventional non-learning based baseline methods.
  arXiv  Detail & Related papers  (2021-07-23T03:33:29Z)
• Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking [59.06167734555191]
  Unmanned Aerial Vehicle (UAV) offers lots of applications in both commerce and recreation. We consider the task of tracking UAVs, providing rich information such as location and trajectory. We propose a dataset, Anti-UAV, with more than 300 video pairs containing over 580k manually annotated bounding boxes.
  arXiv  Detail & Related papers  (2021-01-21T07:00:15Z)
• Perceiving Traffic from Aerial Images [86.994032967469]
  We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images. We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
  arXiv  Detail & Related papers  (2020-09-16T11:37:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.