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增强现实中物体追踪关键技术研究
曹恩源
2022-08-18
Pages78
Subtype硕士
Abstract

随着信息化应用程度的加深,互联网,大数据,5G通信等技术的迅猛发展,信息世界和现实世界之间的联系也越来越紧密。增强现实作为一种将虚拟世界与现实世界融合在一起的技术,正在被广泛应用于工业制造、医疗卫生、文化旅游、互动娱乐等诸多领域。对已知模型的物体进行6自由度的位姿追踪是增强现实的核心环节,只有正确地获取到物体的位姿,才能进一步在物体上融合虚拟信息。随着增强现实相关技术的不断成熟,用户希望应用场景进一步扩大,对增强现实系统的精度和鲁棒性提出了更高的要求,物体追踪技术也面临着新的挑战。传统的物体追踪方法使用手工设计的特征进行匹配,这些方法难以应对弱纹理,遮挡等复杂的应用环境,追踪的精度也受到限制。为获得更高的精度与鲁棒性,近些年来深度学习的方法逐渐流行。但是其计算量庞大,通常难以应用于移动端的增强现实设备。另外,其训练所需的大量时间与数据,也是应用的一大障碍。

针对以上问题,本文将以深度学习算法为基础,研究计算量可以满足增强现实应用的物体追踪算法,且计算过程不依赖物体模型的先验知识,从而对训练外的物体也能够有较好的追踪效果。本文的主要研究内容为:研究了一种基于深度学习的物体追踪算法,一方面,通过分析物体模型的深度投影图,增强算法抗遮挡的能力;另一方面,算法的结构设计与训练方式使其预测结果不依赖待追踪物体模型的先验知识,且拥有更加通用的特征描述能力,也就能够在训练外的物体上也能有较好的表现。最后,为了使算法能够有更小的计算量,同时不影响其精度,本文还提出了一种通过相机参数来恢复视场的图像物体截取方法,使得算法的输入更加精准简洁。

为验证所提出的方法的有效性,本文在三个在物体追踪的经典数据集上进行了实验,并与目前的主流方法进行对比。结果表明,在所选取的对照方法中,本文提出的方法在两个数据集上取得了最高的平均精度,并且可以有效的应对遮挡等复杂的应用场景。为验证本文算法应用于增强现实应用场景的可行性,本文还在移动设备上进行了测试,并成功追踪了真实环境中的物体,证明了本文算法应用于移动端的巨大潜力。

Other Abstract

With the growth of informatization and the rapid development of technologies such as the Internet, big data, and the 5G communication, the connection between the information world and the real world is getting closer and closer. As a technology that integrates the virtual world with the real world, augmented reality is being widely used in many fields such as industrial manufacturing, medical care, cultural tourism, and interactive entertainment. The 6-degree-of-freedom pose tracking of known objects is the core part of augmented reality, and only when the pose of an object is correctly acquired can the virtual information be further fused on the object. As augmented reality-related technologies continue to mature and users wish to further expand the application scenarios, higher requirements are placed on the accuracy and robustness of augmented reality systems, and object tracking technologies are facing new challenges. Traditional object tracking methods use hand-designed features for matching, which are difficult to cope with complex application environments such as weak textures and occlusions, and the accuracy of tracking is also limited. In order to obtain higher accuracy and robustness, deep learning methods have become popular in recent years. However, it is computationally intensive and usually difficult to be applied to mobile augmented reality devices. In addition, the large amount of time and data required for training is also a major obstacle to its application.

To address these problems, this paper will study the object tracking algorithm based on deep learning algorithm, which can meet the computational volume of augmented reality applications, and the computational process does not rely on the prior knowledge of the object model, so that it can have a better tracking effect on the objects outside the training. The main research of this paper is to investigate a deep learning based object tracking algorithm, which on the one hand, enhances the ability of the algorithm to resist occlusion by analyzing the depth projection map of the object model; on the other hand, the structure of the algorithm is designed and trained in such a way that the prediction results do not depend on the prior knowledge of the object model to be tracked, and has a more general feature description capability, so that it can also perform better on objects outside of training. This allows the algorithm to perform better on objects outside of training. Finally, in order to enable the algorithm to have a smaller computational effort without affecting its accuracy, this paper also proposes an image object interception method that recovers the field of view by camera parameters, making the input of the algorithm more accurate and concise.

To verify the effectiveness of the proposed method, experiments are conducted on three classical datasets in object tracking and compared with the current mainstream methods. The results show that among the selected control methods, the proposed method achieves the highest average accuracy on two datasets and can effectively cope with complex application scenarios such as occlusion. To verify the feasibility of applying this paper's algorithm to augmented reality application scenarios, this paper is also tested on mobile devices and successfully tracked objects in real environments, demonstrating the great potential of applying this paper's algorithm to mobile.

Keyword物体追踪 增强现实 深度学习 Transformer 迁移学习
Language中文
Sub direction classification计算机图形学与虚拟现实
planning direction of the national heavy laboratory其他
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49939
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
曹恩源. 增强现实中物体追踪关键技术研究[D]. 模识楼201. 中国科学院自动化研究所,2022.
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