Tracking objects using QR codes and deep learning
Tracking objects using QR codes and deep learning
复制标题
DOI：
10.1117/12.2679910
复制
发表时间：
2023-06
期刊：
影响因子：
--
通讯作者：
A. Ahmadinia;Atika Singh;Kambiz M. Hamadani;Yuanyuan Jiang
中科院分区：
文献类型：
--
作者：
A. Ahmadinia;Atika Singh;Kambiz M. Hamadani;Yuanyuan Jiang
尽管最近在深度学习方面取得了进展，但对象检测和跟踪仍然需要大量的人工和计算工作。首先，我们需要收集并创建一个包含数百或数千张目标对象图像的数据库。接下来，我们必须对图像进行注释或策展，以指示这些图像中目标对象的存在和位置。最后，我们必须训练一个CNN（卷积神经网络）模型来检测和定位新图像中的目标对象。这种训练通常是计算密集型的，由数千个epoch组成，每个目标对象可能需要数十个小时。即使在模型训练完成后，如果实时跟踪和目标检测阶段缺乏足够的准确性，精度和/或速度，对于许多重要的应用程序，仍然有可能失败。在这里，我们提出了一种系统和方法，其通过使用直接安装在待跟踪的实验室工具的表面上的非侵入性对象编码2D QR码，最大限度地减少了上述训练和实时跟踪过程中的各个步骤的计算开销，以用于开发混合现实科学实验室体验的应用。该系统可以立即开始检测和跟踪它，并消除了为每个要跟踪的新实验室工具获取和注释新训练数据集的繁琐过程。
Despite recent advances in deep learning, object detection and tracking still require considerable manual and computational effort. First, we need to collect and create a database of hundreds or thousands of images of the target objects. Next we must annotate or curate the images to indicate the presence and position of the target objects within those images. Finally, we must train a CNN (convolution neural network) model to detect and locate the target objects in new images. This training is usually computationally intensive, consists of thousands of epochs, and can take tens of hours for each target object. Even after the model training in completed, there is still a chance of failure if the real-time tracking and object detection phases lack sufficient accuracy, precision, and/or speed for many important applications. Here we present a system and approach which minimizes the computational expense of the various steps in the training and real-time tracking process outlined above of for applications in the development of mixed-reality science laboratory experiences by using non-intrusive object-encoding 2D QR codes that are mounted directly onto the surfaces of the lab tools to be tracked. This system can start detecting and tracking it immediately and eliminates the laborious process of acquiring and annotating a new training dataset for every new lab tool to be tracked.