Kavli Affiliate: Yi Zhou
| First 5 Authors: Tianchen Deng, Yi Zhou, Wenhua Wu, Mingrui Li, Jingwei Huang
| Summary:
This technical report presents the 1st winning model for UG2+, a task in CVPR
2024 UAV Tracking and Pose-Estimation Challenge. This challenge faces
difficulties in drone detection,
UAV-type classification and 2D/3D trajectory estimation in extreme weather
conditions with multi-modal sensor information, including stereo vision,
various Lidars, Radars, and audio arrays. Leveraging this information, we
propose a multi-modal UAV detection, classification, and 3D tracking method for
accurate UAV classification and tracking. A novel classification pipeline which
incorporates sequence fusion, region of interest (ROI) cropping, and keyframe
selection is proposed. Our system integrates cutting-edge classification
techniques and sophisticated post-processing steps to boost accuracy and
robustness. The designed pose estimation pipeline incorporates three modules:
dynamic points analysis, a multi-object tracker, and trajectory completion
techniques. Extensive experiments have validated the effectiveness and
precision of our approach. In addition, we also propose a novel dataset
pre-processing method and conduct a comprehensive ablation study for our
design. We finally achieved the best performance in the classification and
tracking of the MMUAD dataset. The code and configuration of our method are
available at https://github.com/dtc111111/Multi-Modal-UAV.
| Search Query: ArXiv Query: search_query=au:”Yi Zhou”&id_list=&start=0&max_results=3