单目标跟踪是计算机视觉领域中的研究热点. 传统算法如相关滤波的跟踪速度较快, 但由于提取到的颜色、灰度等手工特征较为粗糙, 跟踪精度往往不高. 近年来随着深度学习理论的发展, 使用深度特征的跟踪方法能够在跟踪的精度和速度方面达到很好的平衡. 本文首先介绍单目标跟踪的相关背景, 接着从相关滤波单目标跟踪、深度学习单目标跟踪两个阶段对单目标跟踪领域发展过程中涌现出的多个算法进行梳理, 并详细介绍目前主流的孪生网络算法. 最后通过大型数据集对近年来优秀算法进行对比分析, 针对其缺点与不足, 对该领域未来的发展前景做出展望.
Single object tracking is a research focus in the field of computer vision. Traditional algorithms including correlation filtering have fast tracking speed but generally low tracking accuracy due to the roughness of extracted manual features such as color and gray levels. With the development of deep learning theory in recent years, tracking methods using deep features can achieve a good balance between tracking accuracy and speed. This study first introduces the relevant background of single object tracking and then sorts out multiple algorithms that have emerged in the development of single object tracking from the two stages of single object tracking based on correlation filters and deep learning. The current mainstream siamese network algorithms are also introduced in detail. Finally, a large data set is used to compare and analyze the excellent algorithms that have emerged in recent years. In view of the shortcomings and deficiencies of these algorithms, the development prospects of this field are provided in this study.