基于特征图的动态筛选策略,用于无人机物体和群体的再识别

Remote. Sens. Pub Date : 2024-02-22 DOI:10.3390/rs16050775
Guoqing Zhang, Tianqi Liu, Zhonglin Ye
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引用次数: 0

摘要

在当代,由于无人驾驶飞行器(UAV)的迅速发展,利用无人驾驶飞行器确保公共安全的潜力巨大。大多数关于无人机图像捕捉的研究主要集中在物体检测和跟踪任务上,但很少有研究关注无人机物体再识别任务。此外,在现实场景中,物体经常成群结队地聚集在一起。因此,重新识别无人机物体和群组是一项重大挑战。本文提出了一种基于特征图框架的新型动态筛选策略,用于无人机物体和群组的重新识别。具体来说,本文提出的基于图的特征匹配模块旨在通过使用相邻的特征节点来增强群组上下文信息的传输。此外,还设计了一种动态筛选策略,尝试修剪未被识别为同一群体的特征节点,以减少噪声(其他群体成员但不属于该群体)的影响。为了验证我们的框架,我们在 Road Group、DukeMTMC Group 和 CUHK-SYSU-Group 数据集上进行了广泛的实验,结果表明与大多数方法相比,我们的框架性能更优。在 CUHK-SYSU-Group、Road Group 和 DukeMTMC Group 数据集上的 Rank-1 分别达到 71.8%、86.4% 和 57.8%。同时,我们还在 PRAI-1581 和 Aerial Image 的无人机数据集、SYSU-MM01 和 CM-Group 的红外数据集以及 RBG-NIR Scene 数据集的近红外数据集上对我们的方法进行了性能测试,意外的发现证明了我们方法的鲁棒性和广泛适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic Screening Strategy Based on Feature Graphs for UAV Object and Group Re-Identification
In contemporary times, owing to the swift advancement of Unmanned Aerial Vehicles (UAVs), there is enormous potential for the use of UAVs to ensure public safety. Most research on capturing images by UAVs mainly focuses on object detection and tracking tasks, but few studies have focused on the UAV object re-identification task. In addition, in the real-world scenarios, objects frequently get together in groups. Therefore, re-identifying UAV objects and groups poses a significant challenge. In this paper, a novel dynamic screening strategy based on feature graphs framework is proposed for UAV object and group re-identification. Specifically, the graph-based feature matching module presented aims to enhance the transmission of group contextual information by using adjacent feature nodes. Additionally, a dynamic screening strategy designed attempts to prune the feature nodes that are not identified as the same group to reduce the impact of noise (other group members but not belonging to this group). Extensive experiments have been conducted on the Road Group, DukeMTMC Group and CUHK-SYSU-Group datasets to validate our framework, revealing superior performance compared to most methods. The Rank-1 on CUHK-SYSU-Group, Road Group and DukeMTMC Group datasets reaches 71.8%, 86.4% and 57.8%, respectively. Meanwhile, our method performance is explored on the UAV datasets of PRAI-1581 and Aerial Image, the infrared datasets of SYSU-MM01 and CM-Group and the NIR dataset of RBG-NIR Scene dataset; the unexpected findings demonstrate the robustness and wide applicability of our method.
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