论文标题
不确定性意识到基于无人机的对象重新识别的多任务金字塔视觉变压器
Uncertainty Aware Multitask Pyramid Vision Transformer For UAV-Based Object Re-Identification
论文作者
论文摘要
在过去的几十年中，通过图像处理和计算机视觉社区对物体重新识别（REID）是生物识别和监视系统中最重要的问题之一。学习强大而歧视性的特征表示是对象REID的关键挑战。在REID中，基于无人机（UAV）的REID更具挑战性，因为图像的特征是飞行无人机的摄像机参数不断变化（例如，视角，高度等）。为了应对这一挑战，已经考虑了多尺度特征表示形式来表征来自无人机在不同高度飞行的图像。在这项工作中，我们提出了一种多任务学习方法，该方法采用新的多尺度体系结构，无卷积，金字塔视觉变压器（PVT），作为基于无人机的对象REID的骨干。通过对类内变化的不确定性建模，我们提出的模型可以使用不确定性感知对象ID和相机ID信息共同优化。在Prai和Vrai上报告了实验结果，这是两个REID数据集，从空中监视中进行了验证我们提出的方法的有效性
Object Re-IDentification (ReID), one of the most significant problems in biometrics and surveillance systems, has been extensively studied by image processing and computer vision communities in the past decades. Learning a robust and discriminative feature representation is a crucial challenge for object ReID. The problem is even more challenging in ReID based on Unmanned Aerial Vehicle (UAV) as the images are characterized by continuously varying camera parameters (e.g., view angle, altitude, etc.) of a flying drone. To address this challenge, multiscale feature representation has been considered to characterize images captured from UAV flying at different altitudes. In this work, we propose a multitask learning approach, which employs a new multiscale architecture without convolution, Pyramid Vision Transformer (PVT), as the backbone for UAV-based object ReID. By uncertainty modeling of intraclass variations, our proposed model can be jointly optimized using both uncertainty-aware object ID and camera ID information. Experimental results are reported on PRAI and VRAI, two ReID data sets from aerial surveillance, to verify the effectiveness of our proposed approach
