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Dataset for Track 5: UAV Tracking and Pose-estimation

Track 5: UAV Tracking and Pose Estimation

About the Dataset

The MMAUD [1] dataset is a dataset dedicated for Unmanned aerial vehicles (UAVs) tracking and position estimation. It provides fisheye camera images, millimeter-wave radar data, and lidar data obtained from a Livox Mid360 and a Livox Avia, with ground truth provided by a Leica Nova MS60 Multi-Station. We intend to fuse data from different modalities to achieve robust UAV position estimation and classification even under challenging conditions.

Note that for this challenge track we have updated the dataset described in the report. The dataset used in this challenge is sampled under a different scene but with the same experimental equipment.

Training & Evaluation

This dataset comprises 102 training sequences and 16 validation sequences, each spanning approximately 20 seconds and 5 seconds, respectively. The data collection involves four distinct UAV types: Mavic 3, M30, M300, and Pham. 4. The final 3D position estimation and classification test would be performed on a hold-out test set of multimodal data from the MMAU dataset. The hold-out test set contain the modality and drone type as the provided training set.

If you have any questions about this challenge track please feel free to email ug2.uav.track.ntu@gmail.com and cvpr2024.ug2challenge@gmail.com

References:
[1] Yuan S, Yang Y, Nguyen T H, et al. MMAUD: A Comprehensive Multi-Modal Anti-UAV Dataset for Modern Miniature Drone Threats[J]. arXiv preprint arXiv:2402.03706, 2024.

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