Multi-view space object recognition and pose estimation based on kernel regression

The application of high-performance imaging sensors in space-based space surveillance systems makes it possible to recognize space objects and estimate their poses using vision-based methods. In this paper, we proposed a kernel regression-based method for joint multi-view space object recognition and pose estimation. We built a new simulated satellite image dataset named BUAA-SID 1.5 to test our method using different image representations. We evaluated our method for recognition-only tasks, pose estimation-only tasks, and joint recognition and pose estimation tasks. Experimental results show that our method outperforms the state-of-the-arts in space object recognition, and can recognize space objects and estimate their poses effectively and robustly against noise and lighting conditions.     

空间目标地基光电探测与识别技术的发展

空间目标地基光电探测与识别技术是空间态势感知的主要技术手段之一。首先归纳了空间目标的典型光学特征,分析了用于轨道与光学特征探测与识别的两类光电望远镜特点,梳理了国外空间目标光电探测与识别技术的发展历程,简要总结了空间目标地基光电探测与识别技术和应用需求间的主要问题,展望了空间目标光电探测与识别技术的未来发展方向。     

红外与可见光图像融合的空间碎片识别方法

针对暗弱空间环境中空间碎片的识别问题,提出了一种光照不均匀环境中的空间碎片识别方法。不同于现有识别方案,该方法从光照不均匀导致空间碎片图像源细节丢失造成识别性能下降的角度出发,首先将空间碎片的红外和可见光图像进行深度融合,并建立空间碎片融合图像数据库,然后基于训练样本采用深度学习技术训练得到空间碎片识别模型。算法分析表明,该图像融合方案具有高度的细节保留能力,识别模型具有在暗弱环境中高精度目标识别能力。最后进行了仿真实验,实验结果表明,该识别方案在姿态变化、图像源亮度变化等干扰条件下都具有较好的鲁棒性。     

基于弹性网稀疏编码的空间目标识别

 传统的特征袋(BoF)模型在目标识别过程中假设每个局部特征点只关联特征词典中一个视觉单词。此外,l₁范数约束下的稀疏编码对于具有较强成对相关性的特征通常只选择一个特征,而不关注哪一个特征被选择。本文提出一种基于弹性网稀疏编码的特征袋模型。该模型利用尺度不变特征变换(SIFT)特征描述子构建特征字典,再通过弹性网回归模型求解每个描述子所对应的稀疏系数向量,最后将目标图像内的稀疏系数向量合并用于分类。与传统的特征袋模型和基于l₁范数稀疏编码的特征袋模型相比,该模型有较好的识别性能,并对视角变化具有较强的鲁棒性。在空间目标图像数据库上的实验验证了该模型的有效性。     

基于红外视觉/激光雷达融合的目标识别与定位方法

近年来,基于可见光图像的目标识别在无人车感知领域得到了广泛应用.然而,可见光图像目标识别无法应用于弱光和黑暗环境.针对于此,提出了一种基于红外视觉/激光雷达融合的目标识别与定位算法.首先,通过基于颜色迁移的数据增强训练方法,提高了红外目标识别算法的泛化性能.继而,提出了一种基于激光雷达修正的单目深度估计方法,通过视觉图...     

一种基于改进核主成分分析的SAR图像识别方法研究

针对传统核主成分分析方法识别SAR图像时,存在图像像素之间关联性差、对目标姿态角依赖性强等局限性,研究了一种基于改进核主成分分析的SAR图像识别方法。其研究思想是,结合SAR图像的特点提出了一种基于局部特征核主成分分析的特征提取方法,并设计了一种基于灰关联分析的双分类器对提取特征进行分类。NSTAR仿真实验表明：该方法不仅可以增强图像像素之间的相关性,而且对目标姿态角不存在依赖性,仿真结果验证了方法的有效性和可行性。     

基于ASM实现视频中物体的定位

对目标进行有效跟踪是目前许多国家获取信息的重要手段,主动形状模型(ASM)主要应用于灰度图像中物体的跟踪与定位,本文针对Koschan等提出的结合金字塔模型的ASM方法作进一步改进,使之应用于视频(本文视频采用MPEG数字视频压缩编码标准)中物体的定位:首先将视频解码还原成图像序列,从人眼视觉特征出发,将RGB颜色模型转换为HSV颜色模型,用色调信息取代灰度信息进行物体定位;并研究图像序列帧之间物体的重合度对提高定位效率的作用.实验结果说明在ASM中使用色调信息能有效地实现视频中的物体定位.     

无先验信息的低轨空间目标航迹起始方法

针对光学观测中缺乏预报信息的低轨道空间目标自动识别捕获问题,提出一种基于修正Hough变换的空间目标航迹起始方法,从而实现空间目标的自动识别。首先利用背景恒星运动规律对观测数据进行预处理,剔除大部分背景恒星。再利用观测数据的时序信息、定向参数进行Hough变换。本方法并没有采用传统的Hough变换对所有参数空间中的数据点进行投票,而是仅对参数空间中部分点投票,可以大幅减少运算量和存储量。试验表明:该方法能够有效地抑制背景恒星产生的虚假航迹,并快速准确地完成无先验信息的低轨道空间目标的航迹起始;可以在光学观测中实现空间目标的自动识别捕获,以及实时发现新目标,这为低轨道空间目标光学观测的无人值守和联网观测奠定了技术基础。     

N-fold Bernoulli probability based adaptive fast-tracking algorithm and its application to autonomous aerial refuelling

Recently, deep learning has been widely utilized for object tracking tasks. However, deep learning encounters limits in tasks such as Autonomous Aerial Refueling (AAR), where the target object can vary substantially in size, requiring high-precision real-time performance in embedded systems. This paper presents a novel embedded adaptiveness single-object tracking framework based on an improved YOLOv4 detection approach and an n-fold Bernoulli probability theorem. First, an Asymmetric Convolutional Network (ACNet) and dense blocks are combined with the YOLOv4 architecture to detect small objects with high precision when similar objects are in the background. The prior object information, such as its location in the previous frame and its speed, is utilized to adaptively track objects of various sizes. Moreover, based on the n-fold Bernoulli probability theorem, we develop a filter that uses statistical laws to reduce the false positive rate of object tracking. To evaluate the efficiency of our algorithm, a new AAR dataset is collected, and extensive AAR detection and tracking experiments are performed. The results demonstrate that our improved detection algorithm is better than the original YOLOv4 algorithm on small and similar object detection tasks; the object tracking algorithm is better than state-of-the-art object tracking algorithms on refueling drogue tracking tasks.     

Distributed sensor data fusion with binary decision trees

A distributed sensor object recognition scheme that uses object features collected by several sensors is presented. Recognition is performed by a binary decision tree generated from a training set. The scheme does not assume the availability of any probability density functions, thus it is practical for nonparametric object recognition. Simulations have been performed for Gaussian feature objects, and some of the results are presented