With the development of science and technology, AR (Augmented Reality) technology is widely used in different fields, such as entertainment, education and medical treatment. When using AR technology, the most critical part is to identify and track objects. Target tracking in augmented reality usually involves many algorithms, such as 2D marking, depth image, SLAM and so on. These algorithms are implemented in different ways, but they all need to consider the size, shape, color and background of the object. Therefore, this task has always been a very challenging part of AR technology research and development.

In recent years, the rapid development of artificial intelligence (AI) technology provides a new choice for the further optimization of AR technology. AI technology can identify and classify objects in images through data analysis and learning. Many studies also show that artificial intelligence technology can significantly improve the accuracy and reliability of identifying and tracking objects.

To develop an AR object recognition and tracking system based on AI technology, the following aspects need to be considered:

First of all, we should choose the AI algorithm suitable for object recognition and tracking in AR environment. At present, the main algorithms used in the industry are based on Deep Convolutional Neural Network (DCNN) and Recurrent Neural Network (RNN). These algorithms can extract different features of objects from a large number of data, such as color, edge and texture, and learn the patterns of objects. Therefore, the size, shape, color and background of the AR object should be considered when choosing the algorithm, so as to obtain better accuracy and stability.

Secondly, it is necessary to provide sufficient quantity and high quality data for AI algorithm. Object recognition and tracking in AR environment need a lot of labeled data. These data can be marked manually or automatically. Manual labeling needs a lot of time and manpower, but it can obtain more accurate and reliable data. Although automatic labeling is fast and convenient, it needs to be screened and verified to ensure the accuracy and reliability of data due to its limitations.

Finally, for the recognition and tracking of AR objects, we need to consider the real-time and stability of the system. AR technology usually needs to run in real time in real-time scene, so the selection and implementation of AI algorithm need to consider factors such as computing speed and memory occupation. At the same time, the stability of the system should be considered to prevent the influence of data error and noise.

To sum up, it is a very promising research to realize more accurate AR object recognition and tracking by using AI technology. Although there are still many challenges in this technology, I believe that with the continuous development and improvement of the technology, it can bring greater impetus to the development and application of AR technology.